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Review on Portable EEG Technology in Educational Research
Abstract
This study reviewed recently published scientific literature on the use of portable EEG technology (PEEGT) in educational research. After systematically searching online bibliographic databases, 22 relevant papers were located and included in the study. The results indicate that (1) PEEGT was mainly used in seven research topics: reading context, presentation patterns of learning materials, interactive behavior, edutainment, e-learning, motor skill acquisition, and promoting learning performance with PEEGT; (2) PEEGT was mainly used to evaluate attention and meditation of participants; (3) most EEG experiments were endured less than 60 min, the sample sizes of EEG experiments were small, the largest research group was university students, portable EEG devices used in educational research were mainly developed by NeuroSky Inc. and Emotiv Inc., and 45% of the papers depicted the validity of PEEGT by citing previous studies and only 3 papers assessed the validity of PEEGT by experiments. As PEEGT is still at its early developing stage, it encounters with two big challenges: considerable measuring errors, and inconvenience to wear in large-scale samples and long-time. It also has some research limitations: not only lack of studies in naturalistic classrooms settings and sports education, but also lack of studies in investigating all cognitive aspects. PEEGT would be employed extensively in education field in the near future if it can address the challenges mentioned above.
... Several studies showed that consumer EEG headsets can be used as research tools (Badcock et al., 2013;Kuber and Wright, 2013;Sawangjai et al., 2019). Portable EEG technology is still at an early stage of development; it encounters many challenges: considerable measurement errors, long setup time in large-scale samples, and inconvenience when wearing it for a long time (Xu and Zhong, 2018). These technical limitations are diminishing, and the announcement of new, lighter, and more comfortable headsets using sensors with better signal detection characteristics appears every year (He et al., 2023). ...
... Several aspects of human cognition were measured by EEG in laboratory conditions, but so far, only a few studies have been conducted in more naturalistic settings (Xu and Zhong, 2018). Portable EEG systems have been widely used in reading contexts. ...
... Usually, the learner's attention level is calculated during web-based reading, using different types of reading materials (books, picture books, etc.) or when learners use sitting, standing, or walking postures for reading. A few studies focused on other teaching and learning subjects, such as mathematics, programming, or science (Xu and Zhong, 2018). Another well -researched area is edutainment; portable EEG systems are used to measure engagement and motivation levels during game -based learning, aiming to achieve an ideal balance between playful and educational content (Gergulescu and Muntean, 2016) or to track progressive improvement in skills of children with special needs (Gallud et al., 2023). ...
Affordable, portable, and easy-to-use devices, as well as computer programs that allow the quantitative analysis of EEG signals, provide the opportunity for wide-ranging educational applications of EEG. However, the use of EEG data is complicated by the presence of significant individual differences in resting activity; therefore, this technology is not suitable for analyzing educational processes on its own. However, when used together with other methods, it could provide valuable data on learning-related brain activity. In our study, we used the Emotiv EPOC EEG headset and monitored brain activity during closed- and open-eye relaxation, reading, and arithmetic calculation. We used the Fourier Transform (FFT) to analyze the spectral content of the EEG signal, determined the power of the theta, alpha, and beta EEG bands, and then calculated the attention, engagement, and cognitive load values for each task. Participants also solved a test related to the tasks, evaluated their performance, and filled out a questionnaire about their impressions of the measurement. We made our conclusions by combining the data from the EEG, the test, and the questionnaire results. We found an increase in the alpha power related to tiredness, increased attention during reading, and high cognitive load during calculation, especially when the result was correct. Participants diagnosed with dyslexia showed lower alpha peak frequency during open-eye relaxation and increased theta activity during reading. Although they needed more time to complete the reading task, their test results were similar to the results of the control group. Our results suggest several possible usage of EEG in education. It enables the continuous monitoring of alertness, can detect fatigue, and helps keep task difficulty at an optimal level. However, the beta theta ratio used in this study to determine attention level does not seem suitable for monitoring sustained attention. EEG can also give us useful insight into the performance of students with special needs, helping teachers provide adequate tasks and guidance.
... With recent development of technologies for psychophysiological measurements of brain activity, including electroencephalogram (EEG), a device that can unobtrusively record an individual's brainwaves in different contexts, researchers and practitioners in different domains have become increasingly interested in analysing brain activity data to understand human psychological processes enacted under different conditions, e.g., epileptic seizures and sleep disorders (for review see [65]). Despite its promises to advance understanding of human psychological processes, including affective and motivational states, to our knowledge, educational researchers have yet to leverage the brain activity data to deepen understanding of CAMM processes within SRL [40]. ...
... Electroencephalogram is a psychological measurement examining the relationship between physiological (i.e., activity of neural cells in brain cortex) and mental processes [65]. The EEG headset contains small electrodes that are placed on the designated positions on the participants' scalp to record the voltage caused by brain activity. ...
... Due to its capabilities to identify psychological processes critical for learning, educational researchers have been increasingly using EEG to study learning in a classroom and lab environments. To date, researchers have appeared to mainly focus on using EEG to measure learners' attentional and meditational constructs [65], while in a limited group of studies researchers have utilised EEG to measure emotional and motivational processes. For example, Salvador Inventado et al. [31] examined the change in student frustration and excitement after receiving feedback as they were studying topics in object-oriented programming. ...
Even though the engagement in self-regulated learning (SRL) has been shown to boost academic performance, SRL skills of many learners remain underdeveloped. They often struggle to productively navigate multiple cognitive, affective, metacognitive and motivational (CAMM) processes in SRL. To provide learners with the required SRL support, it is essential to understand how learners enact CAMM processes as they study. More research is needed to advance the measurement of affective and motivational processes within SRL, and investigate how these processes influence learners' cognition and metacognition. With this in mind, we conducted a lab study involving 22 university students who worked on a 45-minute reading and writing task in digital learning environment. We used a wearable electroencephalogram device to record learner academic emotional and motivational states, and digital trace data to record learner cognitive and metacognitive processes. We harnessed time series prediction and explainable artificial intelligence methods to examine how learner's emotional and motivational states influence their choice of cognitive and metacognitive processes. Our results indicate that emotional and motivational states can predict learners' use of low cognitive, high cognitive and metacognitive processes with considerable classification accuracy (F1 > 0.73), and that higher values of interest, engagement and excitement promote cognitive processing.
... More recently, several companies have developed consumer-grade EEG devices. These devices are compact, wireless, and have a streamlined setup, making them particularly attractive to novice researchers or those looking to collect data outside the traditional laboratory setting [2]. More importantly, consumer-grade devices are cheaper than research-grade devices, allowing those with limited funding an affordable means to collect neurophysiological data. ...
... Clinicians report using the technology to administer neurofeedback therapy [13], facilitate learning [14], assess patient sleep quality [15,16], and determine affective states [17][18][19][20]. Scientists increasingly use consumer-grade devices to collect neural data to address a variety of theoretical and practical research questions [2,21,22]. ...
... For instance, some reviews compared the performance of a single consumer-grade EEG device to non-EEG biosensors in the domains of seizure detection [23], BCI systems [24], and stress recognition [25]. Other reviews have compared multiple consumer-grade EEG devices within a single domain [2,21,[26][27][28]. For instance, Dadebayev et al. ...
Background
Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience.
Purpose
The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG.
Methods
We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author’s country.
Results
We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes.
Conclusions
Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.
... BCI and EEG applications are becoming more popular, notably in educational settings. Nevertheless, the number of studies on this subject is rather modest, and the use of EEG-based systems in learning situations is currently uncommon [22]. Existing studies are primarily measuring students' levels of attention while completing mental activities, with a particular emphasis on attentional and motivational factors (such as reading assignments or seeing instructional material). ...
... Existing studies are primarily measuring students' levels of attention while completing mental activities, with a particular emphasis on attentional and motivational factors (such as reading assignments or seeing instructional material). Only 22 papers on this topic were found in a current search [22] leading researchers to the conclusion that the EEG is primarily used in online learning contexts rather than in traditional offline settings. They have been discussed in relation to motor abilities rather than intellectual abilities more frequently. ...
... The research conducted using various EEG devices in the field of education, which is our main focus, is presented in this part along with related work. As noted in Ref. [22] there are not many studies in the area of educational contexts, although some may be indicated, notably for the assessment of Concentration levels during reading tasks [23,[71][72][73]; Student engagement activities [74]; Feedback personalized [75]; Self-regulation [75]; to research the ideal qualities of learning resources [76][77][78]; to learn more about how students engage with professors and what feedback they provide to inquiries from professors [28,74,79] and in overall e-learning studies [30,80,81]. In Ref. [82] research was done in order to explore software developer's emotions while doing a task of making changes in software. ...
Brain-computer interfaces (BCIs) have diverse applications across various research domains. In healthcare, individuals with disabilities in communication and controlling prosthetic devices are aided. Beyond healthcare, BCIs integrate seamlessly into Internet of Things (IoT) and smart environments, enabling intuitive device control and interaction, enhancing user experiences. In neuromarketing and advertising, BCIs help decipher consumers’ preferences and emotional responses to products and services, providing businesses with profound insights into consumer behavior. In education and self-regulation, BCIs monitor and regulate students’ cognitive states. BCIs use sensors and hardware to capture brain signals, with non-invasive electroencephalography (EEG) technology being a pivotal component. Preliminary studies analyzing cognitive load using EEG signals and the Mindwave device pave the way for measuring student learning outcomes, shedding light on cognitive and neurological learning processes. Our research explores these parameters, particularly the Mindwave system, aiming to understand brain function across domains. To this end, we conduct a range of diversified studies, trying to better grasp parameters such as attention, concentration, stress, immersion, and fatigue during various tasks. Ultimately, our work seeks to harness BCIs’ potential to improve our understanding of brain function and enhance various areas of knowledge.
... Mobile EEG machines are not only portable, but also easy to use (electrode amplifiers eliminate the need for a special gel, while the rigid structure eliminates the need for a cap -required for a stationary EEG equipment) (Huang et al., 2020;Sawangjai et al., 2019). Such tools are therefore used not only in scientific research (Bleichner & Debener, 2017), but also in everyday life, e.g., for studying at school or for checking the fatigue level of truck drivers (Huang et al., 2020;Xu & Zhong, 2018). It is also possible to combine them in groups and study the interaction between several or even a dozen people, e.g., players on the field or students in the classroom (Dikker et al., 2017). ...
... It is also possible to combine them in groups and study the interaction between several or even a dozen people, e.g., players on the field or students in the classroom (Dikker et al., 2017). And although such tools are not without some limitations (Huang et al., 2020;Ratti et al., 2017), they allow an ease, safe and, most important, basically everywhere to observe the work (waves) of the brain (Xu & Zhong, 2018). There are many mobile EEG equipment on the market (Huang et al., 2020;LaRocco et al., 2020;Minguillon et al., 2017;Sawangjai et al., 2019). ...
... However, the most popular is MindWave Mobile EEG (NeuroSky), developed since 2007 (Sawangjai et al., 2019;Xu & Zhong, 2018). The popularity is mainly due to the simplicity and low price (< 250 euro). ...
Brain activity is most often tested in laboratories, while participants sit and sometimes even lie down. Still, although such advanced techniques of studying brain activity as functional magnetic resonance imaging, positron emission tomography, magnetoencephalography or electroencephalography (EEG), help us understand how the nervous system works and what exactly is going on in the brain during many activities, from simple ones, such as hand movements, to highly complex, e.g., arithmetic calculations, they do not allow us to measure brain activity under natural conditions. Thus, there are attempts to build mobile devices, e.g., mobile EEG. In this chapter we show that such a mobile EEG as MindWave expands the territory of brain research-allows for mobile observation of brain activity outside laboratories and therefore in (ordinary) everyday spaces. Moreover, we demonstrate that MindWave is one of the simplest and cheapest mobile EEG, and, at the same time, a very reliable tool, already successfully used in scientific research. In conclusion, we encourage researchers to escape from laboratories (from time to time) and study brain activity in "natural" (not laboratory/artificial) spaces.
... EEG, as a neurophysiological measure with a high temporal resolution (approximately 1 millisecond), presents itself as a suitable choice for assessing instantaneous CL. This capability opens up the opportunity to monitor the dynamic changes in cognitive load on working memory during a cognitive task as numerous portable EEG devices are readily accessible for real-time CL assessment [48]. This method is characterized by its objectivity, noninvasiveness, and relative lack of constraints in comparison to other conventional methods [29]. ...
... This method is characterized by its objectivity, noninvasiveness, and relative lack of constraints in comparison to other conventional methods [29]. The various rhythms generated by electrical brain activity, including delta (1-3 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13), beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50), hold paramount significance in the realm of CL recognition [49]. Notably, the theta and alpha ranges appear to be closely associated with higher brain functions, reflecting task difficulty or CL across diverse task demands [50]- [57]. ...
The increasing prevalence of non-invasive, portable Electroencephalography (EEG) sensors for neuro-physiological measurements has propelled EEG-based assessments of cognitive load (CL) into the spotlight. In this study, we harnessed the capabilities of a four-channel, wearable EEG device that captured brain activity data during two distinct CL states:
Baseline
(representing a non-CL, resting state) and the
Stroop Test
(a CL-inducing state). The primary objective of this study is to estimate the CL index through an innovative approach that employs a hybrid, cluster-based, unsupervised learning technique seamlessly integrated with a 1D Convolutional Neural Network (CNN) architecture tailored for automated feature extraction, rather than conventional supervised algorithms, which facilitated in the acquisition of latent complex patterns without the need for manual categorization. The approach was rigorously evaluated using stratified cross-validation, with several assessment criteria assessing both its quality and predictive capability to estimate the CL index. The results obtained (e.g., homogeneity score of 0.7, adjusted rand index of 0.78, silhouette coefficient of 0.5, and an accuracy rate of 93.2%) demonstrate that our module exhibits superiority over supervised approaches. These results are indicative that the adoption of multi-channel wearable EEG devices may facilitate real-time CL estimation, minimizing the need for extensive human intervention, and reducing potential bias, paving the way for more objective and efficient CL assessments.
... In the majority of studies employing the EEG method, samples of 10-40 participants were utilized in order to enhance the statistical power of the data (Ruff & Huettel, 2014). Xu and Zhong (2018) demonstrated that the sample size of 22 articles scanned for EEG applications in educational research ranged from 80 to 5 subjects (Xu & Zhong, 2018). A study on the recall of NGO advertisements using the EEG method was conducted on 10 participants (Astolfi et al., 2009). ...
... In the majority of studies employing the EEG method, samples of 10-40 participants were utilized in order to enhance the statistical power of the data (Ruff & Huettel, 2014). Xu and Zhong (2018) demonstrated that the sample size of 22 articles scanned for EEG applications in educational research ranged from 80 to 5 subjects (Xu & Zhong, 2018). A study on the recall of NGO advertisements using the EEG method was conducted on 10 participants (Astolfi et al., 2009). ...
One of the primary objectives of non-governmental organizations (NGOs) is to raise awareness and funds for social issues. In this respect, social media represents a crucial tool for NGOs to disseminate their messages to large audiences. This research assumes that the level of impact of message appeals may differ according to the gender of the participants. The aim of this research is to determine the differences in the level of impact of message attractiveness according to the gender of the participants. The social media platform under investigation is Instagram, the second most widely used social media platform in Turkey, according to the We Are Social 2021 social media report. In this context, stimuli for message appeals were selected from the Instagram account of UNICEF Turkey, which is effective in the fight against poverty and is the primary sustainable development goal of the United Nations. The levels of impact of the message appeals on the participants were analyzed in 48 individuals using electroencephalography (EEG), eye tracking, and survey methods. The results of the research indicated that the level of impact of the message attractiveness on the participants differed according to their gender. This suggests that social campaigns may be more effective if their messages are designed with the understanding that they will be perceived differently by the audience. Furthermore, the adoption of personalized advertising activities by NGOs may also be beneficial.
... In clinical settings, EEG is essential for diagnosing and monitoring conditions such as epilepsy and sleep disorders, as well as assessing brain function during cognitive tasks, aiding in the understanding of neural mechanisms underlying perception, memory, and decision-making [6]- [10]. Moreover, EEG is widely applied in non-clinical settings due to its portability and cost-effectiveness, which facilitates monitoring of emotions and stress for mental health management, tracks students' attention in education to enhance the teaching process, and aids in optimizing concentration and relaxation during sports and meditation training for effective outcomes [11]- [13]. However, traditional EEG systems often employ multiple channels to capture a detailed spatial map of brain activity. ...
... Single-channel EEG can assess cognitive load during learning tasks and stages by monitoring changes in brain activity, aiming at optimizing teaching content, methods and outcomes [13]. In a classroom environment, single-channel EEG can monitor changes in attention to teaching content, helping teachers adjust strategies and rhythm to improve outcomes. ...
Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and clinicians. The increasing number and proportion of articles on single-channel EEG underscore its growing potential. This paper provides a comprehensive review of single-channel EEG, focusing on development trends, devices, datasets, signal processing methods, recent applications, and future directions. Definitions of bipolar and unipolar configurations in single-channel EEG are clarified to guide future advancements. Applications mainly span sleep staging, emotion recognition, educational research, and clinical diagnosis. Ongoing advancements of single-channel EEG in AI-based EEG generation techniques suggest potential parity or superiority over multichannel EEG performance.
... This allows for the observation of the brain's potential to understand a concept, contextualize it, and solve a problem. The identification of attention and emotional states associated with brain waves and verified by EEG can facilitate the implementation of more effective teaching-learning processes, resulting in improved outcomes for individuals [3]. ...
... The analysis of brain waves is a well-established field in the area of health, but there has been comparatively little exploration of the potential of this approach in educational environments and classrooms. This highlights the importance of further studies in education [2][3][4]. ...
... In conclusion, multichannel integration of different neurophysiological records is a challenge and an opportunity for psychology [39], especially in learning contexts [21,38,40]. The simultaneous use of a variety of physiological signals allows us to improve our understanding and knowledge of learning processes with the aim of optimizing results [4,18]. ...
... The samples in the studies we reviewed ranged between 10 and 78 subjects, although half of the studies had between 10 and 34. Many authors noted the small number of participants as a limitation in studies with biometric measures [6,22,30,40]. This is also related to other previously noted limitations, such as the majority of subjects not having disabilities [7,16,37] and university students being the largest group being studied [3]. ...
Neurophysiological measures have been used in the field of education to improve our knowledge about the cognitive processes underlying learning. Furthermore, the combined use of different neuropsychological measures has deepened our understanding of these processes. The main objective of this systematic review is to provide a comprehensive picture of the use of integrated multichannel records in higher education. The bibliographic sources for the review were Web of Science, PsycINFO, Scopus, and Psicodoc databases. After a screening process by two independent reviewers, 10 articles were included according to prespecified inclusion criteria. In general, integrated recording of eye tracking and electroencephalograms were the most commonly used metrics, followed by integrated recording of eye tracking and electrodermal activity. Cognitive load was the most widely investigated learning-related cognitive process using integrated multichannel records. To date, most research has focused only on one neurophysiological measure. Furthermore, to our knowledge, no study has systematically investigated the use of integrated multichannel records in higher education. This systematic review provides a comprehensive picture of the current use of integrated multichannel records in higher education. Its findings may help design innovative educational programs, particularly in the online context. The findings provide a basis for future research and decision making regarding the use of integrated multichannel records in higher education.
... Brain activity provides a continuous source of data, that can be analyzed to go deeper and focus on the underlying brain mechanisms. Contemporary neuroscience achieved significant progress in measuring CAs through mental state assessment [16], which became possible thanks to the advances in the portable neuroimaging techniques such as electroencephalography (EEG) or functional near-infrared spectroscopy (fNIRS) [17], [18]. While mental state assessment is possible with just recording and analyzing brain activity, implementation of interactivity, feedback and adaptation to the system can widen its possibilities greatly. ...
... Another issue in this field is related to the technical aspect of the studies. By now a substantial success is achieved in the sphere of neuroimaging techniques, which includes reliable and accessible means of portable neuroimaging [17]. However, the scope and methodological approach in such studies can be spotty. ...
Neuroeducation seeks to implement knowledge about neural mechanisms of learning into educational practice and to understand the impact of learning itself. The crucial tasks in this field are to evaluate and to enhance cognitive abilities, that are used in monitoring educational performance, but also known to greatly impact learning process. Contemporary neuroscience achieved significant progress in measuring brain cognitive abilities through mental state assessment. Popular approach to this task based on brain-computer interface can be difficult to implement in the context of education, but general concept of neuroadaptation is still plausible. In this study, we propose open-loop neuroadaptive system for enhancing student’s cognitive abilities in learning. Assessment of cognitive abilities is based on the concept of executive functions. We design EEG study with special tests and use combined analysis of behavioral and brain activity to assess the level of development of cognitive abilities. Feedback in this system is implemented in the form of recommendations aimed to develop and enhance underdeveloped cognitive abilities and skills. Recommendations have form of various types of extracurricular activities and are based on extensive literature search. This is the system with open-loop adaptation, as it can assess cognitive abilities, provide feedback aimed to enhance these abilities and then after a period of time it can assess cognitive abilities again as a part of the next loop. We believe that developed neuroadaptive system has a potential to be used in educational institutes.
... Pirhonen A. et al. discussed Finnish mobile technology and pointed out that the application of mobile technology in education should be evaluated to determine whether it is suitable for campus education by evaluating whether the technology can fit the goals and spirit of basic education [10]. Xu, J. et al. conducted a review of the literature related to the use of portable computer technology (PEEGT) in education and found that most of the literature describes the effectiveness of PEEGT, and there is a lack of research on naturalistic classroom settings and applications to physical education [11]. Pelánek Radek. ...
... In order to verify the effectiveness and feasibility of the blended teaching model proposed in this paper, this section conducts a practical study on the development of the integration of educational technology and teaching in higher education, using the Information Technology Course of University J as an example. The research period is September-December 2022, and the research object is 120 11 undergraduate students; the online teaching environment is a MOOC platform, and the offline teaching environment is a multimedia classroom. One hundred twenty undergraduate students are divided into three groups, each group has 40 students, in which the online group adopts MOOC teaching mode, the offline group adopts multimedia teaching mode, and the hybrid group adopts blended teaching mode. ...
In the context of education informatization, this paper discusses how to deeply integrate advanced educational technology with traditional teaching. By constructing a blended teaching model with the goal of deep learning and using linear regression, decision tree and other algorithms to build a data analysis framework, this paper aims to assess the practical application effect of educational technology in universities. The empirical study explores the development of deep blending between educational technology and teaching by taking the Information Technology Course of University J as an example. The results showed that after the teaching, the posttest mean of profound learning ability of students in the mixed, online, and offline groups were 1.948, 0.182 and 0.065 higher than the pretest mean, respectively. z-values ranked in the top three were the perseverance of students in the mixed group (-12.642) and the ability to think creatively (-11.682) and critically (-9.875), which were higher than those of students in the online group and the offline group, the overall profound learning ability of students in the mixed group was effectively improved, which verified the effectiveness of the blended teaching model proposed in this paper.
... Electroencephalogram (EEG) is a non-invasive brain imaging technique used in clinical practices and neuroscience research for monitoring, describing, and analyzing the functionality of a brain by acquiring signals generated by the inside electrical activity (Alkan and Kiymik, 2006;Bronzino, 1984;Cohen, 2017;Rana et al., 2017). This technique has been used for over 50 years and has proved to be a vital tool in exploring some key research areas and diseases (Al-Nuaimi et al., 2021;Guarracino, 2008;Mele et al., 2019;Xu and Zhong, 2018). EEG is much cheaper and has higher temporal resolution than functional magnetic resonance imaging (fMRI) (Mele et al., 2019;Cohen, 2017). ...
... EEG is much cheaper and has higher temporal resolution than functional magnetic resonance imaging (fMRI) (Mele et al., 2019;Cohen, 2017). A portable EEG system can record multichannel EEG signals with low power consumption (Xu and Zhong, 2018). Relating to the SpO 2 levels, suppression in neural activity and certain brain frequencies has been observed as measured through EEG (Dean et al., 2003). ...
... A non-systematic review on the use of EEG devices in educational contexts can be seen in Xu and Zhong (2018), although they leave out fundamental works (e.g., Dikker et al., 2017), as well as studies from the last 6 years, important for understanding the possibilities of neuroscientific research in school classrooms (e.g., Bevilacqua et al., 2019;Xu et al., 2022). This article provides a systematic review of the studies from the last 10 years on the use of EEG in educational contexts. ...
This systematic review examines 76 studies that have utilised portable electroencephalographic (EEG) devices in naturalistic and semi‐naturalistic contexts. The review considers themes, purposes, contexts, application populations, device characteristics, and data use. The results show a dominance of studies focused on attention, in technology‐mediated semi‐naturalistic situations, in which records are made individually, with university students using low‐cost equipment with fewer than 15 channels. This review highlights an emerging field within educational research that has not yet been fully integrated into educational practice. However, these first experiences can gradually generate a body of knowledge that will facilitate future applications, together with the development of better and more accessible devices. The use of these devices in educational contexts raises ethical concerns, particularly the influence on teaching decisions by opaque commercial algorithms that may oversimplify assessments of specific cognitive processes and fail to adapt to individual student characteristics.
Context and implications
Rationale for this study: Portable EEG devices are emerging tools that offer new insights into cognitive processes in learning situations.
Why the new findings are important: The findings of this study demonstrate the potential of EEG to monitor aspects such as attention and cognitive load in real time, which could enhance the personalisation of educational strategies.
Implications for educators, researchers and policy makers: This study has implications for educators, researchers and policy makers, as it illustrates how neurotechnology can be integrated into educational settings and emphasises the need for more naturalistic studies to maximise its impact. It also highlights the ethical challenges associated with the use of commercial algorithms in educational decision‐making.
... This is a common elicitation method that allows participants to generate emotional states and label them objectively. When video induction is used, it can stimulate the learning state more effectively because it stimulates both vision and hearing at the same time [3] . ...
In this paper, Electroencephalogram (EEG) is applied in teaching research, and its related fields are studied. At the same time, in order to carry out the fundamental task of cultivating people with three perfections and cultivating people with virtue , this paper also introduces EEG into teaching effect evaluation, so as to adjust teaching design and strategy and improve teaching quality.
... One way to evaluate changes in the central nervous system is measuring brainwave activity. The electroencephalogram (EEG) is an efficient tool for gathering real-time data on brainwave activity [20]. Brainwaves represent the fluctuations in the voltage of current passing through brain neurons and are dependent on the activity of the brain [21]. ...
Background: Musculoskeletal disorders such as cervicogenic headaches present with suboccipital muscle hypertonicity and trigger points. One manual therapy intervention commonly used to target the suboccipital muscles is the suboccipital release technique, previously related to positive systemic effects. Therefore, this study aimed to determine the immediate and short-term effects of the Suboccipital Release Technique (SRT) on brainwave activity in a subgroup of healthy individuals. Methods: Data were collected from 37 subjects (20 females and 17 males, with a mean age of 24.5). While supine, the subjects underwent a head hold followed by suboccipital release. A total of four 15 s electroencephalogram (EEG) measurements were taken and a Global Rating of Change Scale was used to assess self-perception. Results: There was a statistically significant difference (p < 0.005) in various band waves under the following electrodes: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, and FC6. An 8-point range in the Global Rating of Change Scores with a mean score of 1.649 (SD = 1.719 and SE = 0.283) supported the hypothesis of a self-perceived benefit from the intervention. Conclusions: The results of this study indicate that the suboccipital release technique significantly affects brain wave activity throughout different brain regions. This change is likely not the result of any placebo effect and correlates highly with the subject’s self-perception of a change following the intervention. These findings support the clinical use of the suboccipital release technique when a centralized effect is desired.
... BCI offers a novel means of communication and control for individuals with disabilities [5][6][7] and can also enhance the interactions between humans and machines for the broader population [8,9]. In recent years, BCI has seen widespread application across various domains, such as medical rehabilitation [7,10], gaming [11,12], military training [13], psychology [14,15], and education [16,17]. The application of BCIs in human state detection is particularly noteworthy. ...
Brain–computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator’s electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.
... Electroencephalography (EEG) is more widely used in neurocognitive research, brain dynamics, and real-world applications because of its non-invasiveness, portability, compact design, and high temporal resolution [5,6]. Among them, the wireless EEG system is more suitable for use in fields related to daily life research because of its unfettered operation, short preparation time, easy portability, and fewer restrictions on usage scenarios [7]. ...
Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.
... Given the links between action and cognition, it is important to study perceptual and cognitive processes in paradigms that include motion and naturalistic settings (Lang, 1979). The development of portable, low-cost, and commercially available EEG devices has contributed to the study of cognitive processes under a range of naturalistic behaviors (Xu & Zhong, 2018). These devices greatly reduce the cost of research while allowing for the deployment of paradigms away from the laboratory. ...
Although historically confined to traditional research laboratories, electroencephalography (EEG) paradigms are now being applied to study a wide array of behaviors, from daily activities to specialized tasks in diverse fields such as sports science, neurorehabilitation, and education. This transition from traditional to real‐world mobile research can provide new tools for understanding attentional processes as they occur naturally. Early mobile EEG research has made progress, despite the large size and wired connections. Recent developments in hardware and software have expanded the possibilities of mobile EEG, enabling a broader range of applications. Despite these advancements, limitations influencing mobile EEG remain that must be overcome to achieve adequate reliability and validity. In this review, we first assess the feasibility of mobile paradigms, including electrode selection, artifact correction techniques, and methodological considerations. This review underscores the importance of ecological, construct, and predictive validity in ensuring the trustworthiness and applicability of mobile EEG findings. Second, we explore studies on attention in naturalistic settings, focusing on replicating classic P3 component studies in mobile paradigms like stationary biking in our lab, and activities such as walking, cycling, and dual‐tasking outside of the lab. We emphasize how the mobile approach complements traditional laboratory paradigms and the types of insights gained in naturalistic research settings. Third, we discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain–computer interfaces. In summary, this review explores the expanding possibilities of mobile EEG while recognizing the existing challenges in fully realizing its potential.
... Introduction: Electroencephalography (EEG) utilizes non-invasive electrodes placed on the scalp to measure the brain's electrical activity [1]. Traditional EEG measurement methods, involving numerous electrodes and restricting user movement during recording, have made data collection in diverse environments challenging [2]. Recent technological advancements have led to the development of portable and userfriendly EEG devices [3]. ...
The proliferation of portable and wearable electroencephalography (EEG) devices has encouraged EEG research in various areas. These devices, while convenient, often come with limited computational capabilities. However, the challenge of minimizing network complexity for such edge devices was not fully addressed in previous studies. To tackle this, a scalable hybrid network is proposed to classify EEG signals with different demographic factors on edge devices. This model blends a convolutional neural network (CNN) with a self‐attention mechanism in a hybrid block structure. This design alternates between CNN layers and self‐attention layers to efficiently capture both local and global features. In this study, EEG signals acquired using a portable EEG device during gaming session is classified particularly into pre‐puberty and puberty stages. The developed scalable hybrid network (SH‐Net) has shown promising results in distinguishing between pre‐puberty and puberty EEG signals. As a result, the first stage of this model showed higher accuracy compared to other models in 10‐fold cross‐validation: 93.57% for four channels, 89.04% for the frontal lobe channels (AF), and 81.46% for the temporal lobe channels (TP). Notably, the third stage of this model, using the AF channel, achieved higher accuracy compared to other evaluated models that utilized four channels.
... Mobile EEG can be used in educational settings, for example studying reading, presentation patterns of learning materials, interactive behavior, edutainment, e-learning, motor skill acquisition, and promoting learning performance, focusing mostly on attention (Xu and Zhong, 2018). Although EEG recordings at school have been used to capture neural measures of attention in relation to instructional activities (Xu et al., 2022) and academic performance (Fuentes-Martinez et al., 2023), there is no consensus on the measures that are best suited to capture this. ...
The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.
... Among diverse psychophysiological data, EEG data are more commonly utilized to measure attention than HRV data (Schoenberg & David, 2014). However, the mobility of students during learning activities can hinder EEG measurement reliability, leading to a high degree of measurement error and making large-scale, long-term studies challenging (Xu & Zhong, 2018). The scarcity of attention studies using HRV highlights the need for further research in this area. ...
This study provides a comprehensive overview of methodological aspects when using heart rate variability (HRV) measures in educational research. Following PRISMA 2020 guidelines, we searched four databases for relevant studies published until March 5, 2024. From the 48 studies reviewed, we extracted data across three analytical categories: (1) area of study interest and participant populations, (2) data collection and analysis methodologies, and (3) the concurrent and predictive validity of HRV measurement for educational research. Study quality was evaluated using QualSyst assessment criteria. Most studies measured stress and enlisted undergraduate students as participants. Data were predominately collected using wearable devices, measuring HRV for durations of less than 30 min, and in varied contexts, including during exams, while learning, and in experiments. The parameters analyzed varied within both time and frequency domains. HRV data had a moderate level of concurrent validity as a measure of stress in an educational context. The concurrent validity of HRV data for measuring attention remains uncertain with insufficient evidence. Limited correlations appeared between stress and performance. The findings, potentials, and limitations of HRV measures are discussed, and synthesized recommendations for educational research using HRV data are provided.
... The student does not process sentences in calculating. This study has been proven by Xu & Zhong (2018) in Japan who has measured the brain waves of students while carrying out mental arithmetic using an electroencephalography (EEG) device. Bhavya et al. (2022) has stirred up the importance of the use of an abacus in his article entitled "Ancient Abacus: Elegant, Accurate, Fun to Operate: Bead Counting Still Counts in Computer Age". ...
Students' mental mastery in elementary school mathematics lessons in Indonesia is weak, slow, inaccurate, and declining. Mastery problems among elementary school students who have studied mental abacus arithmetic were found to be low. This is an urgent matter to research because there is a gap between theory, expectations, and reality. The purpose of this research was to compare the ability to solve mathematical problems between students who studied abacus mental arithmetic and students who did not study abacus mental arithmetic. This research involved 70 students. Data collection techniques using instruments, the instruments used were the first-semester mathematics exam and mental arithmetic exam. Data analysis techniques using SPSS Version 25.0 statistics, namely the t-test, were used to compare the ability to solve mathematical problems between students who studied mental abacus-arithmetic and students who did not study mental abacus-arithmetic. Pearson correlation was used to determine the relationship between students' mental arithmetic learning achievement and their ability to solve mathematical problems. The results of the research showed that there was a significant difference (p<0.05) in learning achievement on symbolic mathematics questions and mental arithmetic achievement between students who studied mental abacus calculation and students who did not study mental abacus calculation. The minimum score of the group that studied mental abacus calculation was higher compared to the group that did not study mental abacus calculation. However, there was no significant difference (p<0.05) in mathematics learning achievement between students who studied mental abacus-arithmetic and students who did not study mental abacus-arithmetic.
... Within educational innovation, the use of electroencephalography (EEG) for the monitoring of brain signals during learning tests has been proposed, as it has proven to provide a numerical understanding of cognitive processes [11]. By combining this technique with signal analysis and machine learning, it is possible to identify the spectral or temporal variables of the EEG that correlate with increased or reduced cognitive performance, including states of attention and distraction, mental fatigue, and drowsiness, amongst others [12]. ...
This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher–student or student–student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm’s utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts.
... In this study, they compared the engagement of the brain in different presentation modes to correlate it to the learning outcome. Xu et al. [27] reviewed portable EEG devices in educational research and envisioned that portable EEG would be employed extensively in the education field in the near future. ...
Thanks to the rapid growth in wearable technologies and advancements in machine learning, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This paper proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals. Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across 15 participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants’ learning performance increased on average by 26%. Furthermore, to evaluate ERUDITE practicality and scalability, we showed that ERUDITE can be deployed on an edge-based prototype consuming 75 mW power on average with 100 MB memory footprint.
... Among non-invasive neuroimaging techniques, EEG provides a portable, temporally accurate and cost-effective method for research into neural processes and hence is considered as the most practical tool for measuring brain activity changes of children while they engage in a learning task (Xu and Zhong, 2018). Particularly in the context of second language learning, the CRI field can take inspirations from past studies that have examined EEG brain patterns of children associated with language production and comprehension tasks (Maguire and Abel, 2013;Gaudet et al., 2020) to investigate the impact of technology-assisted learning on children's brain. ...
The aim of the current study was to investigate children's brain responses to robot-assisted language learning. EEG brain signals were collected from 41 Japanese children who learned French vocabularies in two groups; half of the children learned new words from a social robot that narrated a story in French using animations on a computer screen (Robot group) and the other half watched the same animated story on the screen but only with a voiceover narration and without the robot (Display group). To examine brain activation during the learning phase, we extracted EEG functional connectivity (FC) which is defined as the rhythmic synchronization of signals recorded from different brain areas. The results indicated significantly higher global synchronization of brain signals in the theta frequency band in the Robot group during the learning phase. Closer inspection of intra-hemispheric and inter-hemispheric connections revealed that children who learned a new language from the robot experienced a stronger theta-band EEG synchronization in inter-hemispheric connections, which has been previously associated with success in second language learning in the neuroscientific literature. Additionally, using a multiple linear regression analysis, it was found that theta-band FC and group assignment were significant predictors of children's language learning with the Robot group scoring higher in the post-interaction word recognition test. These findings provide novel neuroscientific evidence for the effectiveness of social robots as second language tutors for children.
... S. Williams et al., 2020). Portable EEG is also ideal when studying large groups of athletes, as it is less expensive and less time consuming than fMRI (Xu & Zhong, 2018). On the other hand, portable MEG is a better option This document is copyrighted by the American Psychological Association or one of its allied publishers. ...
Mobile electroencephalography and magnetoencephalography technology have the potential to revolutionize the study of motor expertise by providing real-time brain activity data in a noninvasive and portable manner. In the context of sports, these recording techniques have already been used in various applications such as mental fatigue monitoring, concussion assessment, and even talent identification. Here, we discuss the potential for mobile technology to facilitate precise characterization of brain dynamics and outline a number of challenges for the use of portable technology in this context. Specifically, we argue that mobile brain recordings cannot only improve our understanding of motor activities, athletic performance, and athletes’ individual differences but also provide an opportunity for researchers to exploit the richness and uniqueness of sports environments as a tool to better understand the brain. We close with a discussion of the promise of this body of work for future research in sports and exercise neuroscience.
... These studies focus on attention or engagement [17,18], cognitive load, and some basic emotions such as happiness and fear. For example, researchers [19] used an EEG-based brain-computer interface (BCI) to record EEG in the FP1 region to track changes in attention. By utilizing visual and auditory cues, such as rhythmic hand raising, adaptive proxy robots can help students shift their attention when their attention falls below a preset threshold. ...
Human–computer interaction (HCI) plays a significant role in modern education, and emotion recognition is essential in the field of HCI. The potential of emotion recognition in education remains to be explored. Confusion is the primary cognitive emotion during learning and significantly affects student engagement. Recent studies show that electroencephalogram (EEG) signals, obtained through electrodes placed on the scalp, are valuable for studying brain activity and identifying emotions. In this paper, we propose a fusion framework for confusion analysis in learning based on EEG signals, combining feature extraction and temporal self-attention. This framework capitalizes on the strengths of traditional feature extraction and deep-learning techniques, integrating local time-frequency features and global representation capabilities. We acquire localized time-frequency features by partitioning EEG samples into time slices and extracting Power Spectral Density (PSD) features. We introduce the Transformer architecture to capture the comprehensive EEG characteristics and utilize a multi-head self-attention mechanism to extract the global dependencies among the time slices. Subsequently, we employ a classification module based on a fully connected layer to classify confusion emotions accurately. To assess the effectiveness of our method in the educational cognitive domain, we conduct thorough experiments on a public dataset CAL, designed for confusion analysis during the learning process. In both subject-dependent and subject-independent experiments, our method attained an accuracy/F1 score of 90.94%/0.94 and 66.08%/0.65 for the binary classification task and an accuracy/F1 score of 87.59%/0.87 and 41.28%/0.41 for the four-class classification task. It demonstrated superior performance and stronger generalization capabilities than traditional machine learning classifiers and end-to-end methods. The evidence demonstrates that our proposed framework is effective and feasible in recognizing cognitive emotions.
... The revision of the state of art about portable EEG technology in educational research [45] points out that most of the EEG-based studies were conducted with university students, in non-naturalistic classrooms, and as an after-school activity. Just a few of them were made with curricular content. ...
The level of student attention in class greatly affects their academic performance. Teachers typically rely on visual inspection to react to students’ attention in time, but this subjective method leads to inconsistencies across classes. Online education exacerbates the issue as students can turn off cameras and microphones to keep their own privacy. To address this, we present a novel, low-cost EEG-based platform for assessing students’ attention and estimating their academic performance. In a study involving 34 secondary school students (aged 14 to 16), participants watched an academic video and answered evaluation questions while their EEG activity was recorded using a commercial headset. The results demonstrate a significant correlation (0.53, p-value = 0.003) between the power spectral density (PSD) of the EEG beta band (12–30 Hz) and students’ academic performance. Additionally, there was a notable difference in PSD-beta between high and low academic performers. These findings encourage the use of PSD-beta for the immediate and objective assessment of both the student attention and the subsequent academic performance. The platform offers valuable and objective feedback to teachers, enhancing the effectiveness of both face-to-face and online teaching and learning environments.
... Actualmente, los EEG portátiles junto con las interfaces cerebro-computadoras (brain-computer interface, BCI) son más baratos, accesibles, de diseño simple y permiten el rápido estudio del cerebro humano, sin necesidad de tener conocimientos previos sobre electrónica e ingeniería (Dadebayev, Goh y Tan, 2021). En el sector educativo, los EEG se utilizan para realizar un seguimiento del rendimiento de los estudiantes, buscando mejorar la experiencia de aprendizaje (Xu y Zhong, 2018). Sin embargo, existe escepticismo acerca de la utilidad de los EEG portátiles. ...
En este artículo, nos preguntamos: ¿cómo los científicos diseñan nuevos lugares para investigar prácticas sociales? Para dar respuesta, tomamos aportes de los Estudios sobre Ciencia, Tecnología y Sociedad y la Filosofía de la Ciencia. En particular, indagamos en cómo los diversos lugares de investigación condicionan los procesos de producción de conocimientos. Focalizamos en los estudios que analizaron al campo como lugar de investigación y en cómo se articula con las prácticas de laboratorio. Si bien diversos trabajos analizaron las investigaciones en el campo en Argentina, poco problematizaron qué características específicas tiene las prácticas científicas de campo y en cómo permiten transformar prácticas sociales. A partir de tomar como fuentes primarias diversas publicaciones científicas, nuestro caso de estudio son las investigaciones neurocientíficas que utilizan electroencefalogramas (EGG) en aulas. Plantearemos que son los lugares híbridos, entre el campo y el laboratorio, los que permiten investigar prácticas sociales.
Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.
Sosyal kampanyalar üzerinde hangi mesaj çekiciliği türünün daha etkili olduğu literatürde
sıklıkla incelenmektedir. Ancak mesaj çekiciliklerinin etkinliğinin bireysel özelliklere göre
değişebileceği varsayımı henüz yeni tartışılmaya başlanmıştır. Literatür incelendiğinde
ise, özellikle sosyal kampanyalarda sunulan mesajların dikkat çekiciliğinin, yardım
davranışında etkili olan alturistik kişilik özelliğine göre değişebileceğini inceleyen
çalışmalara rastlanılmamıştır. Bu çalışma, yardım kampanyalarında sunulan mesajların
dikkat çekiciliğinin alturistik kişilik özelliğine göre değişebileceğini varsaymaktadır.
Çalışmada, katılımcıların mesaj çekiciliklerine yönelik dikkat düzeylerini belirlemek
adına nörogörüntüleme tekniklerinden EEG (Electroencephalography) yöntemi
kullanılmış ve analizler Spektral Entropi yöntemi ile gerçekleştirilmiştir. Spektral Entropi
yönteminden elde edilen veriler doğrultusunda alturistik ve alturistik olmayan bireylerin
mesaj çekiciliklerine ilişkin dikkat düzeyi farklılıkları SPSS programı aracılığıyla
bağımsız örneklem t-testi ile analiz edilmiştir. Analiz sonuçlarında; olumsuz duygusal,
olumlu duygusal ve hem rasyonel hem duygusal mesajda beynin dikkat ile ilişkili olduğu
prefrontal bölgedeki BA9(AF3-AF4) ve BA8(F3-F4)’de alturistik olan ve olmayan
katılımcılarda farklılaşma olduğu görülürken rasyonel mesajda herhangi bir farklılaşmaya
rastlanmamıştır. Bunun yanı sıra alturistik katılımcıların alturistik olmayan katılımcılara
göre olumsuz ve olumlu duygusal mesaj çekiciliklerine daha fazla dikkat ettiği görülmüştür.
Aynı zamanda altursitik katılımcılar hem rasyonel hem duygusal uyaranın yer aldığı
mesaja da alturisk olmayan katılımcılara göre daha fazla dikkat göstermiştir. Çalışma
sonuçlarının, STK’ların sosyal medyada yardıma eğilimi yüksek olan altursitik bireylere
yönelik dikkat çekici mesaj stratejileri geliştirmesine katkı sağlayacağı düşünülmektedir
Emotion level of the students during their academic sessions has a significant effect on their performance and overall academic grades. The most pre-eminent way of evaluating emotion levels is by analysing the EEG signals obtained from the brain. This paper showcases the experimental study on obtaining the Electroencephalogram (EEG) signals from the students during their academic sessions and classifying it to the types of emotions using machine learning algorithms. This paper explores the method of how the data collection session is conducted and recorded. The data collected is then compiled and divided for the machine learning algorithms. Furthermore, this paper proposed a method of acquiring the emotion labels without prior inducing by using a standard normal distribution method. Finally, this paper also proposed a deep learning neural network model and machine learning models that can be used to determine the emotion level from the EEG signals.
Modern neuroscience is making significant progress in the study of brain functions, which can be of great importance for the education. However, there is a gap between current neuroscientific evidence on how the brain learns and its direct application in classrooms. What can neuroscientists, psychologists, and educators do to improve the interaction between neuroscience and education? In this article, we attempt to answer this question by examining the essentials of educational neuroscience as an interdisciplinary field of research at the intersection of neuroscience, pedagogy, and cognitive science, which seeks to translate research on the neural mechanisms of learning into educational practice and understand the impact of education on the learner's brain. In the first part of the article we describe the origins and current progress of neuroscience in education, discuss terminological uncertainty in the Russian scientific literature on the relationship between neuroscience and education, as well as possible and, in our opinion, most promising ways of interaction between psychology, pedagogy and neuroscience. In the second part of the article we analyze the main directions of contemporary research in the field of neuroscience, based on the Scopus database of scientific information.
Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. In recent years, the emergence of lightweight and portable EEG wearable devices with low-density montages has significantly increased the convenience and usability of BCI applications. However, the use of low-density EEG montages often leads to a loss in EEG decoding performance due to the reduced number of electrodes and limited coverage of scalp regions. To address this issue, we introduce knowledge distillation (KD), a learning mechanism developed for transferring information between neural network models, to enhance the performance of low-density EEG decoding. Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that allows a low-density EEG student model to acquire the inter-sample similarity from a pre-trained teacher model trained on high-density EEG data. The experimental results validate that our SK-KD framework consistently improves motor-imagery EEG decoding accuracy for low-density EEG data and outperforms other KD methods across various model architectures. As the first KD scheme developed for enhancing EEG decoding, we foresee the proposed SK-KD framework facilitating the practicality of low-density EEG-based BCI in real-world applications.
Brain–computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.
A systematic review was carried out to examine the use of robots in early childhood and lower level education. The paper synthesizes the findings of research studies carried out in the last ten years and looks at the influence of robots on children and education. Four major factors are examined - the type of studies conducted, the influence of robots on children's behaviour and development, the perception of stakeholders (parents, children and educators) on educational robots, and finally, the reaction of children on robot design or appearance. This review presents the approach taken by researchers in validating their use of robots including non-experimental (mixed-method, anecdotal, cross-sectional, longitudinal, correlational, and case studies) and quasi-experimental (pre- and post-test). The paper also shows that robot's influence on children's skills development could be grouped into four major categories: cognitive, conceptual, language and social (collaborative) skills. Mixed results are shown when it comes to parents' perception of the use of robots in their children's education while design was shown to influence children's perception of the robot's character or capabilities. A total of 27 out of 369 articles were reviewed based on several criteria.
The use of educational games as a tool for providing learners with a playful and educational aspect is widespread. In this paper, we present an educational game that we developed to teach a chemistry lesson, namely drawing a Lewis diagram. Our game is a 3D environment known as LewiSpace and aims at balancing between playful and educational contents in order to increase engagement and motivation while learning. The game contains mainly five different missions aim at constructing Lewis diagram molecules which are organized in an ascending order of difficulty. We also conducted an experiment to gather data about learners’ cognitive and emotional states as well as their behaviours through our game by using three types of sensors (electroencephalography, eye tracking, and facial expression recognition with an optical camera) and a self report personality questionnaire (the Big Five). Primary results show that a machine learning model namely logistic regression, can predict with some success whether the learner will success or fail in each mission of our game, and paves the way for an adaptive version of the game. This latter will challenge or assist learners based on some features extracted from our data. Feature extraction integrated into a machine learning model aims mainly at providing learners’ with a real-time adaptation according to their performance and skills while progressing in our game.
The research analyses the impact of a teacher’s motivation strategies on the engagement of her students’ based on their preferred motivation type. We propose an innovative framework that combines motivation strategies used by teachers aligned to a continuum of student engagement (Schlechty, 2011). Using student and teacher voice to analyze perceptions about their own motivation type and the intersection/influence of these factors, we report how these may impact on the development and support of authentic student engagement. A qualitative research framework is used to collect data from one class of year five and six students and their teacher. The teacher used intrinsic motivation strategies to engage her students who exhibited a preference for intrinsic or integrated regulated motivation (Ryan & Deci, 2000) and were authentically engaged in response. Students who preferred extrinsic motivation also showed ritual and retreatist forms of engagement, while students demonstrating both intrinsic and extrinsic motivation varied from authentic, ritual, retreatist and rebellious engagement (Schlechty, 2011). The findings reinforce the understanding that if teachers meet students’ needs for competence, relatedness and autonomy then students will become more self determined and thereby authentically engaged in their learning.
In today's stressful circumstances, people are migrating away from conventional cures and methods to check for deeper healing and solutions. People are switching to life and incorporating spirituality into their regular life to make them comfortable and happier. Trying to get some spiritual healing techniques that work, many people have chosen to support and promote this new approach. There is something for everyone.
Examination of motivational dynamics in academic contexts within self-determination theory has centered primarily around both the motives (initially intrinsic vs. extrinsic, later autono- mous vs. controlled) that regulate learners' study behavior and the contexts that promote or hin- der these regulations. Less attention has been paid to the goal contents (intrinsic vs. extrinsic) that learners hold and to the different goal contents that are communicated in schools to in- crease the perceived relevance of the learning. Recent field experiments are reviewed showing that intrinsic goal framing (relative to extrinsic goal framing and no-goal framing) produces deeper engagement in learning activities, better conceptual learning, and higher persistence at learning activities. These effects occur for both intrinsically and extrinsically oriented individu- als. Results are discussed in terms of self-determination theory's concept of basic psychologi- cal needs for autonomy, competence, and relatedness.
Background. Auditory event-related potentials (ERPs) have proved useful in investigating the role of auditory processing in cognitive disorders such as developmental dyslexia, specific language impairment (SLI), attention deficit hyperactivity disorder (ADHD), schizophrenia, and autism. However, laboratory recordings of auditory ERPs can be lengthy, uncomfortable, or threatening for some participants – particularly children. Recently, a commercial gaming electroencephalography (EEG) system has been developed that is portable, inexpensive, and easy to set up. In this study we tested if auditory ERPs measured using a gaming EEG system (Emotiv EPOC®, www.emotiv.com) were equivalent to those measured by a widely-used, laboratory-based, research EEG system (Neuroscan).
Methods. We simultaneously recorded EEGs with the research and gaming EEG systems, whilst presenting 21 adults with 566 standard (1000 Hz) and 100 deviant (1200 Hz) tones under passive (non-attended) and active (attended) conditions. The onset of each tone was marked in the EEGs using a parallel port pulse (Neuroscan) or a stimulus-generated electrical pulse injected into the O1 and O2 channels (Emotiv EPOC®). These markers were used to calculate research and gaming EEG system late auditory ERPs (P1, N1, P2, N2, and P3 peaks) and the mismatch negativity (MMN) in active and passive listening conditions for each participant.
Results. Analyses were restricted to frontal sites as these are most commonly reported in auditory ERP research. Intra-class correlations (ICCs) indicated that the morphology of the research and gaming EEG system late auditory ERP waveforms were similar across all participants, but that the research and gaming EEG system MMN waveforms were only similar for participants with non-noisy MMN waveforms (N = 11 out of 21). Peak amplitude and latency measures revealed no significant differences between the size or the timing of the auditory P1, N1, P2, N2, P3, and MMN peaks.
Conclusions. Our findings suggest that the gaming EEG system may prove a valid alternative to laboratory ERP systems for recording reliable late auditory ERPs (P1, N1, P2, N2, and the P3) over the frontal cortices. In the future, the gaming EEG system may also prove useful for measuring less reliable ERPs, such as the MMN, if the reliability of such ERPs can be boosted to the same level as late auditory ERPs.
Psychophysiological methods, such as electroencephalography (EEG), provide reliable high-resolution measurements of affective player experience. In this article, the authors present a psychophysiological pilot study and its initial results to solidify a research approach they call affective ludology, a research area concerned with the physiological measurement of affective responses to player-game interaction. The study investigates the impact of level design on brainwave activity measured with EEG and on player experience measured with questionnaires. The goal of the study was to investigate cognition, emotion, and player behavior from a psychological perspective. For this purpose, a methodology for assessing gameplay experience with subjective and objective measures was developed extending prior work in physiological measurements of affect in digital gameplay. The authors report the result of this pilot study, the impact of three different level design conditions (boredom, immersion, and flow) on EEG, and subjective indicators of gameplay experience. Results from the subjective gameplay experience questionnaire support the validity of our level design hypotheses. Patterns of EEG spectral power show that the immersion-level design elicits more activity in the theta band, which may support a relationship between virtual spatial navigation or exploration and theta activity. The research shows that facets of gameplay experience can be assessed with affective ludology measures, such as EEG, in which cognitive and affective patterns emerge from different level designs.
Insight represents a special element of knowledge building. From the beginning of their lives, humans experience moments of insight in which a certain idea or solution becomes as clear to them as never before. Especially in the field of visual representations, insight has the potential to be at the core of comprehension and pattern recognition. Still, one problem is that this moment of clarity is highly unpredictable and complex in nature, and many scientists have investigated different aspects of its generation process in the hope of capturing the essence of this eureka (Greek, for “I have found”) moment.
In this paper, we look at insight from the spectrum of information visualization. In particular, we inspect the possible correlation between epiphanies and emotional responses subjects experience when having an insight. In order to check the existence of such a connection, we employ a set of initial tests involving the EPOC mobile electroencephalographic (EEG) headset for detecting emotional responses generated by insights. The insights are generated by open-ended tasks that take the form of visual riddles and visualization applications. Our results suggest that there is a strong connection between insight and emotions like frustration and excitement. Moreover, measuring emotional responses via EEG during an insight-related problem solving results in non-intrusive, nearly automatic detection of the major Aha! moments the user experiences. We argue that this indirect detection of insights opens the door for the objective evaluation and comparison of various visualizations techniques.
Two experiments demonstrated that positive affect fosters intrinsic motivation, as reflected by choice of activity in a free-choice situation and by rated amount of enjoyment of a novel and challenging task, but also promotes responsible work behavior in a situation where the work needs to be done. Where there was work that needed to be done, people in the positive-affect condition reduced their time on the enjoyable task, successfully completed the work task, but also spent time on the more enjoyable task. These results indicate that positive affect does foster intrinsic motivation, and enjoyment and performance of enjoyable tasks, but not at the cost of responsible work behavior on an uninteresting task that needs to be done. Implications for the relationship between positive affect and such aspects of self-regulation as forward-looking thinking and self-control are discussed.
The aim of this study is to evaluate Neuro Sky's Mindset headset as a minimally invasive method of measuring the attention and meditation levels of a subject. Two psychologically-based tests were conducted to assess the suitability of the headset to measure and categorise a user's level of attention and meditation.
Neural signals are everywhere just like mobile phones. We propose to use neural signals to control mobile phones for hands-free, silent and effortless human-mobile interaction. Until recently, devices for detecting neural signals have been costly, bulky and fragile. We present the design, implementation and evaluation of the NeuroPhone system, which allows neural signals to drive mobile phone applications on the iPhone using cheap off-the-shelf wireless electroencephalography (EEG) headsets. We demonstrate a brain-controlled address book dialing app, which works on similar principles to P300-speller brain-computer interfaces: the phone flashes a sequence of photos of contacts from the address book and a P300 brain potential is elicited when the flashed photo matches the person whom the user wishes to dial. EEG signals from the headset are transmitted wirelessly to an iPhone, which natively runs a lightweight classifier to discriminate P300 signals from noise. When a person's contact-photo triggers a P300, his/her phone number is automatically dialed. NeuroPhone breaks new ground as a brain-mobile phone interface for ubiquitous pervasive computing. We discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research.
Moving a brain-computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.
We introduce a user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. Single-channel EEG is recorded using a low-noise dry electrode. Compared to traditional gel-based multi-sensor EEG systems, a dry sensor proves to be more convenient, comfortable and cost effective. A hardware system was built that displays four LED light panels flashing at different frequencies and synchronizes with EEG acquisition. The visual stimuli have been carefully designed such that potential risk to photosensitive people is minimized. We describe a novel stimulus-locked inter-trace correlation (SLIC) method for SSVEP classification using EEG time-locked to stimulus onsets. We studied how the performance of the algorithm is affected by different selection of parameters. Using the SLIC method, the average light detection rate is 75.8% with very low error rates (an 8.4% false positive rate and a 1.3% misclassification rate). Compared to a traditional frequency-domain-based method, the SLIC method is more robust (resulting in less annoyance to the users) and is also suitable for irregular stimulus patterns.
Asymmetrical activity over the frontal cortex has been implicated in the experience and expression of emotions and motivations. Explanations of the research have suggested that relatively greater left frontal activity is associated with positive affect and/or approach motivation, and that relatively greater right frontal activity is associated with negative affect and/or withdrawal motivation. In past research, affective valence and motivational direction were confounded, as only positive (negative) affects that were associated with approach (withdrawal) motivation were examined. Consequently, this research is unable to address whether asymmetrical frontal activity is associated with affective valence, motivational direction, or some combination of valence and motivation. In this article, I review research on the emotion of anger, a negative emotion often associated with approach motivation, that suggests that asymmetrical frontal cortical activity is due to motivational direction and not affective valence. Methodological and theoretical implications for the study of the frontal asymmetry specifically, and for emotion and motivation more generally, are discussed.
Many studies have shown that learners’ sustained attention strongly affects e-learning performance, particularly during online synchronous instruction. This work thus develops a novel attention monitoring and alarm mechanism (AMAM) based on brainwave signals to improve learning performance via monitoring the attention state of individual learners and helping online instructors or teaching assistants to improve the sustained attention levels of learners with low-attention states as they perform online synchronous instruction activities. Totally, 83 and 65 Grade 7 students were randomly assigned to the experimental and control groups that respectively underwent online synchronous instruction with and without AMAM support. Analytical results reveal that the experimental group of learners exhibited significantly better learning performance and sustained attention than those in the control group, verifying that the AMAM efficiently promotes the learning performance and sustained attention of learners. Moreover, the proposed AMAM was more helpful in improving the learning performance of female learners than those of male learners and improved the sustained attention of both male and female learners. Furthermore, the sustained attention, frequency of attention alarms, and learning performance of the learners in the experimental group were strongly correlated, and the sustained attention and frequency of attention alarms strongly predicted learning performance.
In this study, the effectiveness of the application of speech-to-text recognition (STR) technology on enhancing learning and concentration in a calm state of mind, hereafter referred to as meditation (An intentional and self-regulated focusing of attention in order to relax and calm the mind), was investigated. This effectiveness was further explored with regard to foreign language ability and gender. Finally, students’ perceptions towards STR-texts were surveyed. 60 non-native English speaking undergraduates participated in this study. All students were randomly assigned into either a control or an experimental group, with 30 students in each group. Two lectures, both in English but at different levels of difficulty, were given in a classroom environment. Students in the control group received a lecture containing only a video of the instructor and slides; students in the experimental group received the video of the instructor and slides as well as STR-texts of the lecture. The following main findings were obtained: First, STR-texts had a positive effect on the learning performance, attention and meditation of students. In addition, most students had positive perceptions regarding the usefulness of STR-texts for learning. This is because students in the experimental group received instructional content in both verbal (i.e., speech) and visual (i.e., STR-texts) forms, which made the content more comprehensible and easier to process. Second, during lectures with STR, high ability and female students had higher levels of attention and meditation in most cases compared to their counterparts. This finding can be explained by the difference in learning motivation and in the use of learning strategies. That is, high ability and female students are more interested in learning and display greater use of various learning strategies. Based on these results, it is suggested that educators and researchers integrate STR-texts during lectures in English in order to enhance learning and to increase the level of attention and meditation of non-native English speaking students.
In this study, we provide STR-texts to non-native English speaking students during English lectures to facilitate learning, attention, and meditation. We carry out an experiment to test the feasibility of our approach. Our results show that the participants in the experimental group both outperform those in the control group on the post-tests and have higher attention and meditation. In addition, our results demonstrate that lower ability students who used STR-texts had higher learning performance, attention, and meditation than lower ability students in the control group. The results also show that attention and meditation are dynamic and change throughout the lectures. Furthermore, we found that attention trendlines have a negative slope, whereas meditation trendlines have a positive slope. Our results suggest that STR-texts are useful for non-native speaking students, especially those with lower language proficiency, and can enhance their comprehension of English lectures and also raise their attention and meditation. However, educators and researchers should keep in mind that interactive strategies need to be applied in order to maintain and even increase low proficiency student attention throughout the lectures.
This study investigated the effects of attention monitoring with EEG biofeedback on university students' attention and self-efficacy for learning. This study employed a quasi-experimental design, collecting both qualitative and quantitative data. The instruments included an EEG sensor to collect learners' attention levels, the self-efficacy scale from the MSLQ, an anti-phishing achievement test, and an open-ended questionnaire. The participants were 80 university students. A stage-random grouping was adopted to divide the participants into a control group and an experimental group, with 40 participants in each group. The attention monitoring system was developed to monitor anti-phishing learners' attention levels for both the control and the experimental group, and to provide EEG biofeedback for the experimental group when low attention was detected. The results show that there were significant effects on overall attention when EEG biofeedback was provided. No significant differences were found in learners' self-efficacy and achievement between the two groups. However, for the learners in the control group, their self-efficacy for learning was significantly improved. This study found that the types (e.g., auditory or visual, positive or negative) of EEG biofeedback and the selection of the course topics may influence learner's attention, self-efficacy, and learning achievement. This study suggests that when conducting similar studies, it is important to consider the types of EEG biofeedback, the selection of the course topics, and integrating encouraging elements into negative feedback to increase learning outcomes using such a system.
This study aims to design a non-simultaneous distance instruction system with affective computing, which integrates interactive agent technology with the curricular instruction of affective design. The research subjects were 78 students, and prototype assessment and final assessment were adopted to assess the interface and usability of the system. Prototype assessment consisted of heuristic assessment and the system usability scale, while final assessment adopted the triangular cross-validation method: where the questionnaire for user interaction satisfaction, observation, and interviews were used to explore the effect of learning and obtain qualitative and quantitative information for analysis. According to the experimental results, the usability of the non-simultaneous distance instruction system with affective computing was high; the respondents showed high-level satisfaction regarding interaction with the affective learning system; the training game response mechanism of the system could effectively improve the emotion of learning; there was a significant improvement in the effect of learning based on the affective learning system.
Teachers often use in-class questions to examine the level of understanding of their students, while these also enable students to reorganize their acquired knowledge. However, previous studies have shown that students may resist being questioned because of negative emotions. Therefore, this paper proposes the idea of eliciting positive emotions by showing students funny images to reward them for providing correct answers. Three studies were conducted with different courses, using either a video lecture or classroom lecture. The results of Study 1 indicated that 22 learners who watched 10 financial literacy video segments and were rewarded with funny images tended to have more positive emotions, an upward trend in their attention levels, and greater test scores as compared to the other 22 learners watching the same videos who received no rewards. Studies 2 and 3 were conducted in the data structure and computer networks courses, respectively. With regard to affective states, the 52 students in the two courses gained more learning confidence in classes that providing the amusing stimuli as compared to those without such rewards. However, only students in the computer networks course had higher test scores on their mid-term exams when answering rewarded questions compared to when they answered those questions without rewards, although this was not found with the students in the data structure course. The findings suggest that rewarding students with amusing stimuli can enhance students' affective states, even though this instructional strategy does not lead to higher learning performance all the time.
This study investigates the relationship between visual attention and reading time using a mobile electroencephalography device. The mobile electroencephalography device uses a single channel dry sensor, which easily measures participants’ attention in the real-world reading environment. The results reveal that age significantly influences visual attention and reading time. Adults’ visual attention and reading time are superior to those of children; furthermore, the visual attention and reading time of adults are positively correlated, indicating that the executive function of attention and reading time increases with age. Regarding gender differences, boys’ visual attention is superior to that of girls, whereas female adults exhibit higher visual attention than male adults. Moreover, multimedia picture books substantially enhance boys’ visual attention, whereas girls exhibit low visual attention. This result indicates that multimedia picture books with sound, light, and animation effects are more appealing to boys and thus enhance boys’ attention. 2016
Engagement influences participation, progression and retention in game-based e-learning (GBeL). Therefore, GBeL systems should engage the players in order to support them to maximize their learning outcomes, and provide the players with adequate feedback to maintain their motivation. Innovative engagement monitoring solutions based on players’ behaviour are needed to enable engagement monitoring in a non-disturbing way, without interrupting the game-play and game flow. Furthermore, generic metrics and automatic mechanisms for their engagement monitoring and modelling are needed. One important metric that was used for engagement modelling is TimeOnTask, which represents the duration of time required by the player to complete a task. This paper proposes ToTCompute (TimeOnTask Threshold Computation), a novel mechanism that automatically computes - in a task-dependent manner - TimeOnTask threshold values after which student engagement decreases with a given percentage from his initial level of engagement (e.g., after 2 min student engagement will fall with 10 % from his initial level). In this way the mechanism enables engagement modelling at a higher granularity and further enables engagement-based adaptation in GBeL systems. ToTCompute makes use of game-playing information and EEG signals collected through an initial testing session. The results of an experimental case study have shown that ToTCompute can be used to automatically compute threshold values for the TimeOnTask generic engagement metric, which explains up to 76.2 % of the variance in engagement change. Furthermore, the results confirmed the usefulness of the mechanism as the TimeOnTask threshold value is highly task-dependent, and setting its value manually for multiple game tasks would be a laborious process.
Rapid progress in information and communication technologies (ICTs) has fueled the popularity of e-learning. However, an e-learning environment is limited in that online instructors cannot monitor immediately whether students remain focus during online autonomous learning. Therefore, this study tries to develop a novel attention aware system (AAS) capable of recognizing students' attention levels accurately based on electroencephalography (EEG) signals, thus having high potential to be applied in providing timely alert for conveying low-attention level feedback to online instructors in an e-learning environment. To construct AAS, attention responses of students and their corresponding EEG signals are gathered based on a continuous performance test (CPT), ie, an attention assessment test. Next, the AAS is constructed by using training and testing data by the NeuroSky brainwave detector and the support vector machine (SVM), a well-known machine learning model. Additionally, based on the discrete wavelet transform (DWT), the collected EEG signals are decomposed into five primary bands (ie, alpha, beta, gamma, theta, and delta). Each primary band contains five statistical parameters (including approximate entropy, total variation, energy, skewness, and standard deviation), thus generating 25 potential brainwave features associated with students' attention level for constructing the AAS. An attempt based on genetic algorithm (GA) is also made to enhance the prediction performance of the proposed AAS in terms of identifying students' attention levels. According to GA, the seven most influential features are selected from 25 considered features; parameters of the proposed AAS are also optimized. Analytical results indicate that the proposed AAS can accurately recognize individual student's attention state as either a high or low level, and the average accuracy rate reaches as high as 89.52%. Moreover, the proposed AAS is integrated with a video lecture tagging system to examine whether the proposed AAS can accurately detect students' low-attention periods while learning about electrical safety in the workplace via a video lecture. Four experiments are designed to assess the prediction performance of the proposed AAS in terms of identifying the periods of video lecture with high- or low-attention levels during learning processes. Analytical results indicate that the proposed AAS can accurately identify the low-attention periods of video lecture generated by students when engaging in a learning activity with video lecture. Meanwhile, the proposed AAS can also accurately identify the low-attention periods of video lecture generated by students to some degree even when students engage in a learning activity by a video lecture with random disturbances. Furthermore, strong negative correlations are found between the students' learning performance (ie, posttest score and progressive score) and the low-attention periods of video lecture identified by the proposed AAS. Results of this study demonstrate that the proposed AAS is effective, capable of assisting online instructors in evaluating students' attention levels to enhance their online learning performance.
The reading development of children depends on various sensory stimuli, which help them construct reading contexts and facilitate active learning and exploration. This study uses sensory stimuli provided by picture books using various forms of media to improve children's concentration performance. We employ picture books using four forms of media: conventional picture books, pop-up books, talking books, and e-books. Children in young (Grade 3) and senior elementary school students (Grade 6) are recruited as participants. The NeuroSky MindBand (electroencephalogram) is used as a tool to measure children's concentration. The results indicate that children in Grade 3 express higher concentration and interest than children in Grade 6 in picture books, and the effect of gender is greater than that of grade on the level of concentration invested in the picture book media form and exhibits a significant interaction and effects on children's concentration performance. As a result, we suggest that talking books or e-books, which provide multisensory stimuli, should be selected for elementary school boys. In addition, talking books should be selected for girls in young elementary school students, while conventional books should be used for girls in senior elementary school students to avoid distraction caused by excessive media stimuli.
Despite the popularity of mobile reading devices, many studies have indicated that small screens restrict information transmission, adversely affecting reading performance on mobile devices. Moreover, mobile reading typically occurs in different reading contexts. Therefore, suitable text display type for mobile reading in different reading contexts should be considered, such that learners can effectively read content. This work selected the frequently used static and dynamic text display types (i.e. Paging and Auto-scrolling) and assessed their effects on mobile reading performance. Furthermore, the features of static and dynamic text display types were considered when designing a mixed text display type for mobile reading. Based on brainwave detector, reading-comprehension test sheet, and cognitive-load scale, this work designed a mobile reading experiment with a two-factor experimental design to assess the effects of the selected static, dynamic, and designed text display types, which were respectively presented in sitting, standing, and walking contexts, on reading comprehension, sustained attention, and cognitive load of learners. Experimental results show that sitting obtained the highest sustained attention and the mixed text garnered lowest sustained attention. Moreover, although analytical results show that no significant difference existed in overall reading comprehension with each text type presented with the mobile reading contexts, reading comprehension (i.e. memory, comprehension, and application types) was significantly affected by the reading context, and to some degree, the text display type. This work also found that the text display type is a major factor affecting learners' cognitive load; however, learners' cognitive load is not be affected by the considered reading contexts. Among the three text display types, the mixed type generates the highest cognitive load, followed by the dynamic type, and then the static type. In conclusion, the three reading contexts with the three text display types have both advantages and disadvantages for reading comprehension, sustained attention, and cognitive load. As a result, text display type for mobile reading on small screens should be adjusted according to reading context or to improve reading comprehension, attention, or cognitive load.
The purpose of this chapter is to introduce novice and intermediate EEG researchers to a convenient and user-friendly EEG system from NeuroSky, Inc. In our recent study we were interested in changes in the frontal cortical EEG activity of healthy adults as a function of accommodative stress during performance of a sustained attention task. We used a commercially available low-cost wireless EEG device from NeuroSky (MindSet), which has a single active Fp1 dry electrode capable of recording research-grade EEG coupled with powerful noise-filtering and data software support. The convenience and ease-of-use of MindSet is further enhanced with validated eSense meters of Attention and Meditation. In this chapter we also provide additional data analytic support for EEG power spectrum using SPSS syntax commonly used in many biobehavioral sciences.
Learner's motivation is one of the main aspects that need to be addressed for a successful learning process. Consequently, learner motivation assessment and measurement have attracted significant research interest in the e-learning area in general and game-based learning in particular. Traditional methodologies for learner motivation analysis rely on data collected through questionnaires. However, this approach does not fit well in the context of game-based learning, because an out-of-game questionnaire breaks the game user's flow and immersion. This paper presents a novel electroencephalography (EEG) sensor-based methodology that supports real-time non-disturbing automatic measurement and analysis of learner's motivation in game-based learning.
An evaluation case study with participants playing an educational game was conducted in order to investigate the feasibility of the proposed sensor-based methodology and to compare the proposed methodology with the intrinsic motivation inventory-based questionnaire methodology. The results analysis has shown that the sensor-based methodology outperforms the traditional questionnaire-based methodology. The traditional questionnaire-based methodology is limited to analysing learner's motivation on short game-playing durations, while losing its feasibility when analysing learner's overall motivation over a long game-playing duration. Results have shown that learner's motivation changes in time during the game-play period and the learner's self-report of his/her motivation, assessed through the questionnaire-based methodology, tends to reflect only the last moments before the questionnaire is answered. Conversely, the proposed EEG sensor-based methodology is more suitable to analyse learner's motivation on both short and long game-playing durations (e.g. game tasks, game levels, etc.), with the additional benefit of not interrupting the game-play and not breaking the game flow and the learner's immersion with the game.
Although online courseware often includes multimedia materials, exactly how different video lecture types impact student performance has seldom been studied. Therefore, this study explores how three commonly used video lectures styles affect the sustained attention, emotion, cognitive load, and learning performance of verbalizers and visualizers in an autonomous online learning scenario by using a two-factor experimental design, brainwave detection, emotion-sensing equipment, cognitive load scale, and learning performance test sheet. Analysis results indicate that, while the three video lecture types enhance learning performance, learning performance with lecture capture and picture-in-picture types is superior to that associated with the voice-over type. Verbalizers and visualizers achieve the same learning performance with the three video types. Additionally, sustained attention induced by the voice-over type is markedly higher than that with the picture-in-picture type. Sustained attention of verbalizers is also significantly higher than that of visualizers when learning with the three video lectures. Moreover, the positive and negative emotions induced by the three video lectures do not appear to significantly differ from each other. Also, cognitive load related to the voice-over type is significantly higher than that with by the lecture capture and picture-in-picture types. Furthermore, the cognitive load for visualizers markedly exceeds that of verbalizers who are presented with the voice-over type. Results of this study significantly contribute to efforts to design of video lectures and also provide a valuable reference when selecting video lecture types for online learning.
Flow is an optimal experience that results in intense engagement in an activity. In computer-based instructional environment, flow can be used to examine learning performance. We used questionnaire survey and electroencephalography (EEG) analysis to examine the influence of challenge-skill balance on the flow experience and influence of flow experience on learning performance in a computer-based instructional environment. The results showed that the flow experience of learners depends on challenge-skill balance of learning materials. The research explored the possibility of using an inexpensive non-medical EEG device to research the association between flow experience and challenge-skill balance in educational information systems.
In this paper, a concentration evaluation of reading behaviors with electrical signal detection on the head is presented. The electrode signal is extracted by brain-computer-interface (BCI) to monitor the user's degree of concentration, where the user is reminded by sound to concentrate, or teaching staffs are reminded to help users improve reading habits, in order to facilitate the user's ability to concentrate. The digital signal processing methods, such as the Kalman Filter, Fast Fourier Transform, the Hamming window, the average value of the total energy of a frame, correlation coefficient, and novel judgment algorithm are used to obtain the corresponding parameters of concentration evaluation. Users can correct their manner of reading with reminders. The repeated test results may be expected to lie with a probability of 95%. Such model training results in better learning effect.
Due to the rapid development of information technology, web-based learning has become a dominant trend. That is, learners can often learn anytime and anywhere without being restricted by time and space. Autonomic learning primarily occurs in web-based learning environments, and self-regulated learning (SRL) is key to autonomic learning performance. Moreover, sustained attention to web-based learning activities can be challenging for students. Therefore, a web-based reading annotation system with an attention-based self-regulated learning mechanism (ASRLM), which is based on brainwave detection, is designed to enhance the sustained attention of learners while reading annotated English texts online, and thereby promote online reading performance. In total, 126 Grade 7 students in four classes at a junior high school in New Taipei City, Taiwan, are the participants. Among the four classes, two classes are randomly distributed to the experimental group and the other two classes are randomly distributed to the control group. The experimental group utilizes the ASRLM to support their reading of annotated English texts online, whereas the control group is not supported by the ASRLM while reading annotated English texts online. Experimental results show that sustained attention and reading comprehension of the experimental group are better than those of the control group. Moreover, the web-based reading system with ASRLM support promotes the sustained attention and reading comprehension of female learners more than those of male learners while reading annotated English texts online. Additionally, learners with high-SRL ability in the experimental group have better sustained attention and reading comprehension than those learners with low-SRL ability. Furthermore, the sustained attention and reading comprehension of the experimental group are strongly correlated, and the duration of sustained attention strongly predicts their reading comprehension performance.
This study investigates the modulation of frontal EEG dynamics with respect to progress in motor skill acquisition using a wireless EEG system with a single dry sensor. Participants were required to complete repeated trials of a computerized visual-motor task similar to mirror drawing while the EEG was collected. In each trial, task performance of the participants was summarized with a familiarity index which took into account the performance accuracy, completion rate and time. Our findings demonstrated that certain EEG power spectra decreased with an increase in motor task familiarity. In particular, frontal EEG activities in delta and theta bands of the whole trial and in gamma band in the middle of the trial are having a significant negative relationship with the overall familiarity level of the task. The findings suggest that frontal EEG spectra are significantly modulated during motor skill acquisition. Results of this study shed light on the possibility of simultaneous monitoring of brain activity during an unconstrained natural task with a single dry sensor mobile EEG in an everyday environment.
Purpose
The EPOC neuroheadset is a commercially available device that allows game players to control a computer using their facial expressions or their thoughts. This paper aims to examine whether it has the potential to be used as an input for assistive technology (AT) devices.
Design/methodology/approach
Two experiments were conducted. In the first, 12 non‐impaired subjects used the neuroheadset to control a computer with their facial expressions. They also used a simple system of two head switches for comparison. In the second experiment, three non‐impaired subjects were trained to use the neuroheadset to control a computer with their thoughts.
Findings
In the first experiment, the neuroheadset was slower and less accurate than the head switches (p<0.05), and was also harder to use. It is unlikely to be preferred to existing methods of accessing AT for those that retain a small amount of head movement. In the second experiment, by the end of the week, all three subjects achieved accuracy rates greater than chance.
Research limitations/implications
All subjects were non‐impaired, and the sample size in the second experiment was small. Further research should concentrate on the second experiment, using larger sample sizes and impaired subjects.
Practical implications
The EPOC neuroheadset is substantially cheaper than similar specialist devices, and has the potential to allow those with no voluntary muscle control to access AT with their thoughts.
Originality/value
The results of these two experiments show that the Emotiv EPOC neuroheadset can be used as an interface for non‐impaired users to transfer information to a computer, which could in turn be used to control AT.
EEG-based monitoring the state of the users brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as Attention Deficit Hyperactivity Disorder (ADHD), where concentration function deficit exists, Autism Spectrum Disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR) and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.
The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC ( http://emotiv.com/index.php ) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation. STATEMENT OF RELEVANCE: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.