Article

Review on Portable EEG Technology in Educational Research

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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.

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... While literature exists that explores how data collection using functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG), two popular non-invasive modes of measuring the neural basis of behavior and cognition, can measure attention and engagement in learning environments, not many resources exist that apply these two to learning outcomes. In fact, most literature surrounding EEG and fMRI in learning environments tends to focus on attention and motivation of learners (Xu & Zhong, 2018). Additionally, measuring the neural basis of learning can be difficult in authentic situations, as data collection can be intrusive (Poulsen et al., 2017;Seghier et al., 2019;Xu & Zhong, 2018). ...
... In fact, most literature surrounding EEG and fMRI in learning environments tends to focus on attention and motivation of learners (Xu & Zhong, 2018). Additionally, measuring the neural basis of learning can be difficult in authentic situations, as data collection can be intrusive (Poulsen et al., 2017;Seghier et al., 2019;Xu & Zhong, 2018). Moreover, the research that focuses on EEG and fMRI in technology-based learning environments is sparse, as is literature that focuses on a variety of educational disciplines (Seghier et al., 2019;Xu & Zhong, 2018). ...
... Additionally, measuring the neural basis of learning can be difficult in authentic situations, as data collection can be intrusive (Poulsen et al., 2017;Seghier et al., 2019;Xu & Zhong, 2018). Moreover, the research that focuses on EEG and fMRI in technology-based learning environments is sparse, as is literature that focuses on a variety of educational disciplines (Seghier et al., 2019;Xu & Zhong, 2018). In this literature review, the researcher will seek to answer the following research questions to determine the state of the literature and where gaps are that warrant additional study: 1) How has fMRI and EEG been used to assess educational effectiveness in both traditional and technology-based learning environments? ...
Conference Paper
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... That is, a higher level of relaxation indicates that an individual is more relaxed and less stressed [43]. There are different cognitive aspects that can be measured with EEG devices [37], such as reading context [35], presentation patterns [37], interactive behavior [35], edutainment [44], e-learning [45,46], motor skill acquisition [47], and promoting performance [48]. We can measure different brain waves, such as alpha, beta, gamma, delta, and theta waves. ...
... That is, a higher level of relaxation indicates that an individual is more relaxed and less stressed [43]. There are different cognitive aspects that can be measured with EEG devices [37], such as reading context [35], presentation patterns [37], interactive behavior [35], edutainment [44], e-learning [45,46], motor skill acquisition [47], and promoting performance [48]. We can measure different brain waves, such as alpha, beta, gamma, delta, and theta waves. ...
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Conference Paper
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... The quasi-dry electrode has a hydrated local skin interface with a moisturizing solution drawn from a reservoir inside the electrode. The significant advantages of quasi-dry electrodes include maintenance of lower electrode impedance similar to wet electrodes, with reduced discomfort compared to dry electrodes (Xu and Zhong, 2018). Quasi-dry electrodes also allow long-term EEG measurements due to the small amount of moisturizing solution that spreads and dries on the scalp less than typical wet electrode conductive gel (Mota et al., 2013). ...
... Active electrode designs can also enable skin-electrode impedance monitoring periodically throughout a data recording session to ensure low scalp-electrode impedance and high signal quality are preserved over time (Patki et al., 2012). Compared to standard passive EEG electrode configurations, each active electrode amplifier is electrically powered, often requiring additional wiring (Xu and Zhong, 2018). ...
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... Electroencephalography as a neurophysiological measure with a high temporal resolution (approximately 1 ms) is a well-suited candidate for the assessment of cognitive load in educational environments because this method is objective, non-invasive, and less restricted in comparison to other neuroimaging methods (Antonenko et al., 2010). Nowadays, many portable EEG devices can be easily used in classrooms for cognitive load assessment (Xu and Zhong, 2018). Moreover, it has a high temporal resolution which is a good property for the assessment of instantaneous cognitive load. ...
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... BCI is a type of psychophysiological measurement used to examine the relationships between mental and bodily processes, which can automatically measure the learner's attention and meditation levels in real-time in naturalistic classrooms settings. However, this technology is still inconvenient to use in large-scale samples and longtime along with large measurement errors (Xu & Zhong, 2018). ...
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... The overall system includes an FE-based circuit, three flexible elastomeric electrodes for the scalp, and a skin electrode. It presents a remarkable reduction of electromagnetic and noise interference compared to standard EEG systems [131,[133][134][135]. The device works on the SSVEP-BCI paradigm and has been tested on six human subjects for real-time controlling of a wireless wheelchair, slide changing of presentation software, and wireless mini-vehicle. ...
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... On the other hand, laboratory equipment such as the 64-channel brain cap and signal amplifiers are unwieldy, which cannot be easily transfered from location to location. Fourth, the qualification of subjects should be further determined [42]. In this paper, the subjects were selected randomly from a narrow range, i.e., mainly from our university. ...
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Thesis
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... Recent developments in mobile EEG technology have allowed for the collection of high-quality data in real-world settings, expanding the possibility for researchers to examine neural processes underly learning in the classroom. Yet, research using mobile EEG systems to evaluate emotion, motivation, and attention in educational settings has been conducted mainly with adults (Xu & Zhong, 2018). With young children, these methods have been most frequently applied to the study of clinical populations (e.g., ASD and children with epilepsy) in either laboratories or controlled field studies using lab-based paradigms (Williams et al., 2019;Zheng et al., 2017). ...
Article
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... Mobile EEG systems are increasingly used in educational technology research. Of 22 studies reviewed by Xu and Zhong (2018), all used off-the-shelf consumer-grade systems, with 82% opting for a Neurosky system with only one dry electrode on the forehead. The majority (91%) of the authors used automatically calculated indices of "meditation" or "attention" provided by the system's proprietary algorithms, instead of (pre)processing the raw EEG data themselves. ...
Article
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... [90] research showed improved amplitude and synchronization in various frequencies of cognitive tasks and oscillatory activities, particularly in theta (4-7 Hz) and alpha . EEG is also used for educational purposes for understanding student mental status by examine and observe students' brain activity [92]. ...
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Article
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The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration.
... Performing this task relies on several cognitive processes, including visual search, working memory, and mental arithmetic, so the ST is appropriate for the stated goals. Monitoring cortical activity during cognitive task completion with an EEG system allows obtaining objective information about the brain's functioning and the occurring cognitive processes with a reasonable spatial and good time resolution [14][15][16]. At the same time, electroencephalography is an easy-to-use and safe non-invasive technology that is especially important when working with children. ...
Article
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In this paper, we used an EEG system to monitor and analyze the cortical activity of children and adults at a sensor level during cognitive tasks in the form of a Schulte table. This complex cognitive task simultaneously involves several cognitive processes and systems: visual search, working memory, and mental arithmetic. We revealed that adults found numbers on average two times faster than children in the beginning. However, this difference diminished at the end of table completion to 1.8 times. In children, the EEG analysis revealed high parietal alpha-band power at the end of the task. This indicates the shift from procedural strategy to less demanding fact-retrieval. In adults, the frontal beta-band power increased at the end of the task. It reflects enhanced reliance on the top–down mechanisms, cognitive control, or attentional modulation rather than a change in arithmetic strategy. Finally, the alpha-band power of adults exceeded one of the children in the left hemisphere, providing potential evidence for the fact-retrieval strategy. Since the completion of the Schulte table involves a whole set of elementary cognitive functions, the obtained results were essential for developing passive brain–computer interfaces for monitoring and adjusting a human state in the process of learning and solving cognitive tasks of various types.
... Common artifacts (e.g., eye-blinks and muscle activities) were filtered out and a special two-tiered system was implemented to analyze EEG engagement levels and identify significant drops in attention in real-time (Szafir & Mutlu, 2012). The β/(α+θ) index retrieved from cross-channel EEG data can also reflect motivation and task engagement (Xu & Zhong, 2018). High task engagement is characterized by increased beta, and decreased alpha and theta. ...
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More recently, Explainable Artificial Intelligence (XAI) research has shifted to focus on a more pragmatic or naturalistic account of understanding, that is, whether the stakeholders understand the explanation. This point is especially important for research on evaluation methods for XAI systems. Thus, another direction where XAI research can benefit significantly from cognitive science and psychology research is ways to measure understanding of users, responses and attitudes. These measures can be used to quantify explanation quality and as feedback to the XAI system to improve the explanations. The current report aims to propose suitable metrics for evaluating XAI systems from the perspective of the cognitive states and processes of stakeholders. We elaborate on 7 dimensions, i.e., goodness, satisfaction, user understanding, curiosity & engagement, trust & reliance, controllability & interactivity, and learning curve & productivity, together with the recommended subjective and objective psychological measures. We then provide more details about how we can use the recommended measures to evaluate a visual classification XAI system according to the recommended cognitive metrics.
... Nowadays, more and more research and commercial applications use PEEGT as a measurement tool. e utilization of PEEGT significantly expands the application of neurophysiological measurements and dramatically increases the practicability of neurometric equipment, such as in marketing [5], management [9], education [10], and engineering [11]. e PEEGT effectively reduces the threshold of the experimental environment in business and management situations. ...
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As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects’ activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant’s blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs.
... The BookRoll logs on the same timeline provided an additional channel of reading interaction data. Based on the EEG signals, online Meditation, Engagement and Attention values ranging from 0 to 100 were either exported or computed as in Xu et al. (2018). The heatmaps of the eye tracking data along with the screen capture video was recorded using the Tobii Ghost application and Open Broadcaster Software (OBS). ...
Conference Paper
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Multi-modal analytics has the potential for understanding learning activities and possibly supporting the process based on the interpretation of the signals captured. However, the integration of multiple data sources remains an issue. While some commercial packages assist researchers to organize the data for their specific study, infrastructure is still not available to integrate learning logs and physiological sensor data together for the same learning episode. In our prior work, GOAL system solves the issue of data integration by connecting sensor data from wearable activity trackers by API and linking the data to the learners registered in a learning management system through LTI. In this work, we extend the functions to collect EEG, GSR and eye tracking data from physiological sensors within the same technical infrastructure. A pilot study was conducted to synchronize data during a reading-based learning task and discuss the capabilities of such a platform for designing learning support at scale within our learning and evidence analytics framework (LEAF).
... On cognitive features, EEG signals are used to evaluate the student's level of meditation (Xu and Zhong 2018) and attention (Brawner and Avelino 2016), further combined with an audio signal to increase the level of attention (Sun and Yeh 2017). Thermal infrared imaging can also obtain students' level of attention within a smart classroom (Kim ...
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This work presents a real-time biofeedback tool that uses wearables and Internet of Things (IoT) for applications in education. Using wearables (electroencephalography helmet, smart bands) and a Raspberry Pi, signals were integrated in real-time. Moreover, a three-class random forest (RF) classification machine learning (ML) algorithm, based on the aforementioned signals, which predicted a student's mental fatigue (none, moderate and extreme fatigue) with 92.69% average correct classification percentage using 5-fold cross-validation (CV). The system can evaluate a student's performance under different learning modalities, in addition, it can show different content types to students, depending on the professor's necessities. In the current work, vehicle signals were integrated for teaching automotive engineering.
... Different ways of measuring attention exist, including eye tracking, fMRI, EEG, and self-report. The present study used the portable EEG method, which has been proved to be a reliable way to measure attention in real-world learning situations [24][25][26] and self-reports of attention (i.e., the distracted time and SCL) [21,27]. Target performance was measured with tests based on the class materials. ...
Article
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Online courses are prevalent around the world, especially during the COVID-19 pandemic. Long hours of highly demanding online learning can lead to mental fatigue and cognitive depletion. According to Attention Restoration Theory, ‘being away’ or a mental shift could be an important strategy to allow a person to recover from the cognitive overload. The present study aimed to test the interleaving strategy as a mental shift method to help sustain students’ online learning attention and to improve learning outcomes. A total of 81 seventh-grade Chinese students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). We collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data during the 40 min of online learning, and test results to assess learning outcomes. The results showed that the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning online classes.
... However, most current EEG measurement devices require professional assistance due to their large size and the complex installation procedures [10]. For this reason, most of the devices are used for medical or experimental purposes only, not for everyday life [11,12]. Therefore, for everyday-use EEG systems, the main research goal is to implement miniaturized and easy-to-use EEG hardware by (1) decreasing the size of the electrodes, (2) designing small-form-factor integrated circuits (IC), and (3) simplifying the structures of the EEG device. ...
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As more people desire at-home diagnosis and treatment for their health improvement, healthcare devices have become more wearable, comfortable, and easy to use. In that sense, the miniaturization of electroencephalography (EEG) systems is a major challenge for developing daily-life healthcare devices. Recently, because of the intertwined relationship between EEG recording and processing, co-research of EEG recording hardware and data processing has been emphasized for whole-in-one miniaturized EEG systems. This paper introduces miniaturization techniques in analog-front-end hardware and processing algorithms for such EEG systems. To miniaturize EEG recording hardware, various types of compact electrodes and mm-sized integrated circuits (IC) techniques including artifact rejection are studied to record accurate EEG signals in a much smaller manner. Active electrode and in-ear EEG technologies are also researched to make small-form-factor EEG measurement structures. Furthermore, miniaturization techniques for EEG processing are discussed including channel selection techniques that reduce the number of required electrode channel and hardware implementation of processing algorithms that simplify the EEG processing stage.
... In order to test this hypothesis, we monitor the attention levels in a class of primary school students during their classroom learning based on a wearable EEG device. The wearable EEG device was used since it is able to monitor attention level continuously and non-intrusively (Chen & Wang, 2018;Davidesco, 2020;Xu & Zhong, 2018). Then, we calculated the degree of similarity between the attention dynamics of each student and the class-average attention dynamics (termed as 'inter-brain attention coupling') and compared its effectiveness in predicting academic performances with the widelyused attention level for each student (termed as 'single-brain attention level). ...
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Learning-related attention is one of the most important factors influencing learning. While technologies have enabled the automatic detection of students' attention levels, the detected states might fail to be learning-related if students did not attend learning tasks (e.g., the attention level of a student who reads comics secretly during classroom learning). This phenomenon poses challenges to the practical application, especially in the primary school stage, which is crucial for students to set up learning attitudes/strategies. Inspired by the emerging inter-person perspective in neuroscience, we proposed an inter-brain attention coupling method to detect learning-related attention. Our method is based on the premise that learning-related attention should follow the structures of course contents, which is reflected in the attention dynamics shared across students. We hypothesized that one's level of learning-related attention could be detected in the inter-brain attention coupling, which is defined as the degree to which an individual student's attention dynamics match the attention dynamics averaged across classmates. To test this idea, wearable EEG devices were used to monitor students' attention levels in a class of primary school students during classroom learning. We found that one's inter-brain attention coupling was positively correlated with academic performance: higher performances are associated with higher coupling to the class-average attention dynamics. No significant correlation was found between students' attention levels averaged within the individual and their academic performances. These results demonstrated the value of inter-brain attention coupling in assessing primary school students' learning process. We argued that inter-person coupling analysis could be useful in monitoring learning-related attention in real-world educational contexts.
... With the former group, the variables are directly linked to the central nervous system, that is, the measurements require invasive techniques for the target student, limiting their comfort and deploying possibilities of these systems on a large scale. Illustrative examples can be found in [7,8], whose authors employ electroencephalography (EEG) to determine attention and learning levels. On the other hand, the latter group concerns the ANS signals, which are commonly represented and modeled using facial expressions, eye-based measurements, Heart Rate (HR), Blood Volume Pressure (BVP), and Skin Conductance (SC). ...
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Teaching is an activity that requires understanding the class’s reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel’s model, we grouped the most important Ekman’s facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students’ level of attention for in-presence lectures.
... While the measurement of electrophysiological process parameters can be more easily done due to the comparatively low cost of EEG measurements and the mobility of EEG systems (see Xu & Zhong, 2018, for a current application in educational research), individual assessments using neuroimaging methods (e.g., fMRI) seem (at least today) rather utopian due to the additional time and financial effort involved. ...
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Human intelligence represents one of the most investigated and validated constructs in psychological research. The validity of intelligence tests is, however, regularly questioned, especially when it comes to cross-cultural research. Although various alternatives and further developments of intelligence assessments have been proposed (e.g., culture-fair tests), there are still many fundamental measurement issues in cross-cultural research in need for a solution. The present article addresses this topic from the perspective of cognitive psychology and neuroscience to propose a process-oriented and biologically-inspired approach of intelligence assessment as a potential solution. We demonstrate the importance of elementary cognitive processes (e.g., working memory capacity, attention, information processing speed) underlying individual differences in intelligence and emphasize that the distinction between contents and processes plays a central role in the assessment of intelligence. From a cognitive and neuropsychological perspective, it can be assumed that especially processes lend themselves to cross-cultural comparison research, whereas contents should be understood as being rather culturally specific. We discuss three different approaches to improve the comparability of intelligence assessments across different cultures and argue that future intelligence research should combine knowledge from different scientific disciplines to identify such intelligence-relevant cognitive processes. Finally, we evaluate the potential of a process-oriented and biologically inspired intelligence assessment against the background of its current possibilities and its challenges.
... is research includes the early detection of personality disorders, emotional distress, and other mental illnesses [22,23]. During a pandemic, the psychological consequences are to improve people's quality of life and to maximize their performance [24,25]. ...
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COVID-19, a WHO-declared public health emergency of worldwide concern, is quickly spreading over the world, posing a physical and mental health hazard. The COVID-19 has resulted in one of the world’s most significant worldwide lockdowns, affecting human mental health. In this research work, a modified Long Short-Term Memory (MLSTM)-based Deep Learning model framework is proposed for analyzing COVID-19 effect on emotion and mental health during the pandemic using electroencephalogram (EEG) signals. The participants of this study were volunteers that recovered from COVID-19. The EEG dataset of 40 people is collected to predict emotion and mental health. The results of the MLSTM model are also compared with the other literature classifiers. With an accuracy of 91.26%, the MLSTM beats existing classifiers when using the 70–30 partitioning technique.
... EEG records brain activities through multiple electrodes placed on the scalp. It has already been used in research on presence [8][9][10][11]14,15] and in other fields [16][17][18][19][20]. ...
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Presence is the sense of being in a virtual environment when physically situated in another place. It is one of the key components of the overall virtual reality (VR) experience, as well as other immersive audio applications. However, there is no standardized method for measuring presence. In our previous study, we explored the possibility of using electroencephalography (EEG) to measure presence by using questionnaires as a reference. It was found that an increase in the subjective presence level was correlated with an increase in the theta/beta ratio (an index derived from EEG). In the present study, we re-analyzed the original data and found that the peak alpha frequency (PAF), another EEG index, may also have the potential to reflect the change in the subjective presence level. Specifically, an increase in the subjective presence level was found to be correlated with a decrease in PAF. Together with our previous study, these results indicate the potential use of EEG for the objective measurement of presence in the future.
... Eine Messung der neuronalen Prozessparameter ist vergleichsweise aufwendiger. Während die Messung elektrophysiologischer Prozessparameter aufgrund der vergleichsweise geringen Kosten von EEG-Messungen und der prinzipiellen Mobilität von EEG-Systemen in der Praxis durchaus denkbar wäre (siehe Xu & Zhong, 2018, für aktuelle Anwendungsbeispiele in der Bildungsforschung), scheint eine Individualdiagnostik mit Hilfe bildgebender Verfahren derzeit aufgrund des zeitlichen und finanziellen Mehraufwandes eher utopisch. ...
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One of the challenges of immersive technology research is that its increasing system complexity makes evaluating the user experience difficult. The use of an electroencephalogram (EEG) has been suggested as a promising approach to understanding the user’s cognitive, emotional, and behavioral responses to immersive technology. However, the translation of this method into clear applications for user research remains challenging. To address this challenge, this paper outlines a systematic literature review to identify the applications of EEG measures currently adopted in immersive technology research. The full range of journal articles and major conference proceedings that reference the adoption of EEG measures to address immersive technology usage issues were searched. Based on rigorous inclusion and exclusion criteria, 84 relevant papers were identified and reviewed in the study. This literature review involves analysis of bibliometric data, research contexts, EEG analysis methods, and EEG stimuli. Presented in this paper are research gaps identified and opportunities for future research recommended based on the analysis results. This study contributes to advancing our knowledge about how to collect and analyze EEG data in immersive technology research.
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Soft skills training is considered important for employees to be successful at work. Several companies are offering immersive virtual soft skills training with head-mounted displays. The main contribution of this paper is to provide an overview of the research literature within the field of using immersive virtual soft skills learning and training of employees. The results of this preliminary scoping review show that there is a lack of research literature and empirical studies within this topic.
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The 7th International Conference of the Immersive Learning Research Network (iLRN 2021) is an innovative and interactive virtual gathering for a strengthening global network of researchers and practitioners collaborating to develop the scientific, technical, and applied potential of immersive learning. It is the premier scholarly event focusing on advances in the use of virtual reality (VR), augmented reality (AR), mixed reality (MR), and other extended reality (XR) technologies to support learners across the full span of learning—from K-12 through higher-education to workbased, informal, and lifelong learning contexts. Following the success of iLRN 2020, our first fully online and in-VR conference, this year’s conference was once again based on the iLRN Virtual Campus, powered by ©Virbela, but with a range of activities taking place on various other XR simulation, gaming, and other platforms. Scholars and professionals working from informal and formal education settings as well as those representing diverse industry sectors are invited to participate in the conference, where they may share their research findings, experiences, and insights; network and establish partnerships to envision and shape the future of XR and immersive technologies for learning; and contribute to the emerging scholarly knowledge base on how these technologies can be used to create experiences that educate, engage, and excite learners.
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Smart people are very important to our future because only humans can utilize technology and improve economic and political efficiency, and play a role in social, cultural and urban progress. However, low moral intelligence, low skilled manpower and conflicts between multi-ethnic are major problems that often lead to social issues. The objectives of the study were to identify smart people concept for a smart city and examine the elements of smart people in Pengerang. The study was conducted using a mixed-method. The data were collected using question- naires, document reviews and observations. The results showed that the agreeable- ness, conscientiousness, emotional stability, extraversion and experience to openness elements recorded mean score at a high level. Besides that, the element of agree- ableness recorded as the highest average mean score with 3.78 rather than the other four elements, conscientiousness, extraversion, emotional stability and experience to openness. The implications of this study show that the local authorities and govern- ment need to draw the strategies and policies for build-up smart people in order to develop and promote a smart city in Malaysia.
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While it is widely acknowledged that different disciplines are associated with distinct characteristics that would require different learning strategies and approaches, how the disciplinary differences are manifested in students’ daily learning process remain largely underexplored. The present study investigated the disciplinary differences from the framework of hard and soft disciplines, by recording thirty-four high-school students’ EEGs simultaneously in real-classroom settings during their regular Chinese (a soft discipline) and Math (a hard discipline) courses across a whole semester. Inter-brain coupling analysis was conducted to identify the neural correlates of successful learning for different disciplines. One’s theta-band inter-brain coupling to all their classmates during both the Chinese and the Math sessions was found to be positively correlated with their final exam scores of Math, while one’s alpha-band inter-brain coupling to their excellent peers was positively correlated with their final exam scores of Chinese. These results demonstrated the value of inter-brain coupling for reflecting students’ learning outcomes for both hard and soft disciplines and highlighted excellent peers as a good candidate to represent a successful learning process for soft disciplines. Our findings provided a piece of neuroscience evidence towards the understanding of disciplinary differences during real-world classroom learning.
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Smart environment in a smart city is the changes of a city and shape the pure environment with the element to achieve a human settlement. This change is achieved by extensive and persuasive infrastructure and building which give a big impact on the environment. However, the lack of a smart environment regulatory framework was increasing the level of difficulties for the implementation of smart environmental practices. The objectives of the study were to identify the smart envi- ronment concept for a smart city and examine the elements of the smart environment in Pengerang. The study was conducted by using a mixed method. The data was collected by using questionnaires, document reviews and observations. The results showed that the quality of life, facilities and automated system element recorded mean score at a high level. Besides that, the element of quality of life recorded the highest average mean score with 4.48 rather than the other four elements, facilities, awareness, safety and automated system. The implications of this study show that the implementation needs deep planning by local authorities and government to realize the changes toward a smart environment.
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Smart economy has emerged as part of the smart city framework to encourage urban growth which the urban population currently lives in a digital society. The smart economy has emerged as part of the smart city framework to encourage urban growth. However, with technological and economic shifts brought about by globalization, cities are now facing the challenges of simultaneously sustaining productivity and sustainable urban development. The objectives of the study were to identify the smart economy concept for a smart city and examine the elements of smart economy in Pengerang. The study has conducted by using a mixed method. The data have collected by using questionnaires, document reviews and observations. The results showed that respondents fully understood the concept of smart economy that enables, encourages and stimulates economic activity in Pengerang. Besides, there will be future strategies and initiatives in order to encourage people to implement the smart economy. The implications of this study show that people need to pay attention to the issues and strategies that have been proposed by the government to implement and promote the smart economy toward a smart city.
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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.
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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.
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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.
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