Conference Paper

BRAVO: a brain virtual operator for education exploiting brain-computer interfaces

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Abstract

This paper introduces a new e-learning system that works with a Brain-Computer Interface to customize the educational experience, according to user's reactions and preferences.

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... "Let's Learn" by Andujar and Gilbert [7] is another EEG-augmented reading system, similar to "Focus", which uses content-related videos from YouTube to improve users' engagement, although no formal study was performed to validate the system. Finally, "Bravo" [26] estimates user's attention and meditation levels and presents users with learning material that results in high engagement. ...
... The engagement index also measures vigilance and alertness, which are required in a learning process at the micro grain (individual) scale. A variety of experiments used this approach as a way to measure engagement and attention-related features [4,7,11,24,26,39]. We based our system on prior research that reported using consumer EEG headbands with 1 to 6 channels. ...
... We based our system on prior research that reported using consumer EEG headbands with 1 to 6 channels. They are currently being widely used to detect cognitive engagement in the learning domain [1,4,7,24,26], as well as in other domains [6,21,40]. We use the BrainCo headband called Focus 1 (Figure 3, left), a lightweight EEG device with 3 hydrogel electrodes [35]. ...
Article
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Information about a person’s engagement and attention might be a valuable asset in many settings including work situations, driving, and learning environments. To this end, we propose the first prototype of a device called AttentivU—a system that uses a wearable system which consists of two main components. Component 1 is represented by an EEG headband used to measure the engagement of a person in real-time. Component 2 is a scarf, which provides subtle, haptic feedback (vibrations) in real-time when the drop in engagement is detected. We tested AttentivU in two separate studies with 48 adults. The participants were engaged in a learning scenario of either watching three video lectures on different subjects or participating in a set of three face-to-face lectures with a professor. There were three conditions administrated during both studies: (1) biofeedback, meaning the scarf (component 2 of the system) was vibrating each time the EEG headband detected a drop in engagement; (2) random feedback, where the vibrations did not correlate or depend on the engagement level detected by the system, and (3) no feedback, when no vibrations were administered. The results show that the biofeedback condition redirected the engagement of the participants to the task at hand and improved their performance on comprehension tests.
... 10 School Student [165]; [166]; [167]; [168]; [169]; [170]; [171]; [172]; [173]; [174]; [175]; [176]; [177]; [178]; [179]; [180]; [181]; [182]; [183]; [184]; [185]; [186]; [187]; [120]; [35]; [188]; [189]; [190]; [191]; [192] 30 University / College Student [193]; [194]; [195]; [196]; [197]; [198]; [199]; [200]; [201]; [202]; [203]; [204]; [205]; [206]; [207]; [48]; [208]; [209]; [115]; [210]; [211]; [212]; [213]; [214]; [215]; [167]; [216]; [217]; [218]; [219]; [220]; [119]; [221]; [222]; [108]; [223]; [224]; [225]; [226]; [227]; [228]; [176]; [229]; [230]; [231]; [232]; [233]; [234]; [235]; [236]; [237]; [238]; [239]; [240]; [241]; [242]; [243]; [244]; [245]; [246]; [247]; [248]; [249]; [250]; [251]; [139]; [135]; [112]; [138]; [252]; [253]; [254]; [255]; [256]; [257] [258]; [259]; [136]; [260]; [137]; [261]; [262]; [263]; [264]; [265]; [266]; [267]); [268]; [114]; [269]; [270]; [117]; [271]; [116]; [272]; [273]; [141]; [274]; [143]; [275]; [276]; [144]; [277]; [278]; [279]; [280]; [281]; [282]; [283]; [284]; [285]; [145]; [286]; [287]; [288]; [289]; [290]; [291]; [292]; [293]; [294]; [295]; [296]; [297]; [298]; [299]; [300]; [301]; [302]; [192] ; [303]; [304]. ...
... Individual and Behavioral [251]; [139]; [135]; [112]; [138]; [252]; [253]; [254]; [255]; [257]; [256]; [258]; [259]; [136]; [260]; [137]; [261]; [243]; [262] ; [263]; [264]; [265]; [266]; [267]; [268]; [181]; [182]; [183]; [113]; [114]; [269]; [270]; [184]; [117]; [115]; [271]; [116]; [272]; [273]; [141]; [35]; [189]; [191]. ...
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Brain–computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching–learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is insufficient literature on BCI that addresses students with disabilities in the learning process. Further, our analysis revealed a bias toward the significance of cognitive process factors compared with other influential factors, such as the learning environment and emotions that influence learning. Finally, we concluded that BCI technology could improve students’ learning and cognitive skills—when consistently associated with the different pedagogical teaching–learning strategies—for better academic achievement.
... In addition our solution has a good level of generality because it can exploit in modular ways on the one hand various types of sensors and on the other hand various e-learning applications. Thus, it is more general than solutions that just connect a sensor with an application to obtain ad-hoc adaptation as in Bravo [6]. ...
... The adaptation engine is aware whether one or more notification is associated to the triggering of adaptation rules concerning the current application (because it hosts an association table for this purpose). When a rule is triggered, the changes associated to the corresponding actions are transmitted (6) in JSON format to a script injected in the e-learning application. The main aim of this script is to identify the DOM elements that have to be modified client side (7) according to the changes specified in the adaptation rules. ...
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Technology to make physiological measurements related to attention and cognitive load is becoming more affordable. We propose a solution based on combining the exploitation of dynamic user information gathered through such technology with a rule-based strategy for adaptation of e-learning Web applications. We focus on users' physiological data and aspects relevant for the task being carried out. A flexible rule-based approach allows designers and developers to define a wide range of rule compositions to express changes in the user interface based on how the user feels and behaves. The overall goal of the framework is to serve as a tool for content developers of Web applications, such as operators of online Learning Management Systems, and for their end-users. In this domain, through our approach teachers can create their educational contents, and specify how they should dynamically adapt to students' behaviour in order to improve the learning process.
... Diverse BCI based solutions have been proposed to perform automatic adaptation of content according to the engagement level of the user [1][2][3]. Marchesi and Ricco [1] introduced an e-learning system that operates with BCI to straighten the educational experience. The adaptation is based on the reactions of the user and preferences. ...
... Diverse BCI based solutions have been proposed to perform automatic adaptation of content according to the engagement level of the user [1][2][3]. Marchesi and Ricco [1] introduced an e-learning system that operates with BCI to straighten the educational experience. The adaptation is based on the reactions of the user and preferences. ...
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Brain-Computer Interface can read brain signals and transform them into readable information. It can be used in the education field to enhance learning capabilities. For instance, an instructor can use such device to track interest, stress level, and engagement of his students to adapt his teaching approach. We propose in this paper a mobile learning system that can automatically adapt its content to keep students engaged while the instructor is explaining the material. The main aim of our system is to enhance those children learning capabilities, engagements, thinking, and memorization skills.
... In fact, BCI devices, such as the Emotiv Epoch we used, allow freedom of movement (communicates with the server, using a bluethoot dongle) and reduce individuals' anxiety, thanks to wireless collection of the EEG signal and to the ergonomy of the device. BCIs process brainwaves into digital signals and it is used extensively in neuroscience [16,19], cognitive science [20,21,22,23] and cognitive psychology [24,25] research, as well as in games and education [26,27] and some other applications, such as attention training [28,29] music training and analysis [30,31]. ...
... M. Marchesi [4] proposed the use of EEG signals to interact with multimedia. In our experiment, we implement our original EEG-based interactive movie application, called nMovie, with electrocardiogram side channel. ...
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Pervasive brain mobile interfaces can be made more accurate and time efficient when knowledge from other sensors in the Internet of Things infrastructure are utilized. This paper takes the example of Neuro-Movie (nMovie), an interactive movie application that blurs video frames based on mental state, to illustrate and analyze optimization opportunities when BMoI is interfaced with IoT. The three way trade-off between accuracy, energy efficiency, and real-time operation can be optimized through usage of physiological responses from IoT sensors and prediction algorithms. Experiments on 10 volunteers show that: a) utilizing electrocardiogram responses to psychological stimulus increases the accuracy of mental state detection by almost 10%, b) predictive models cover computation and communication latencies in the system to satisfy real-time requirements, and c) use of predictive models allows duty cycling of smartphone WiFi communication that can potentially save upto 50% energy.
... The main goal of NPs is the development of medical applications, such as neurally controlled limb prostheses for paralyzed patients. Additionally, NP approach finds applications in such areas as computer gaming (Mason, Bohringer et al. 2004, Finke, Lenhardt et al. 2009, Martisius and Damasevicius 2016, safety systems that monitor drivers' state of wakefulness (Picot, Charbonnier et al. 2008, Liu, Chiang et al. 2013, Garces Correa, Orosco et al. 2014) and even education (Marchesi and Riccò 2013). Some view NP as a technology for augmenting brain functions (Maguire and McGee 1999, Farah and Wolpe 2004, Madan 2014, Zehr 2015. ...
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Neural prostheses (NPs) link the brain to external devices, with an eventual goal of recovery of motor and sensory functions to patients with neurological conditions. Over the past half-century, NPs have advanced significantly from the early ideas that sounded like science fiction to the modern high-tech implementations. In particular, invasive recordings using multichannel implants have enabled real-time control of artificial limbs by nonhuman primates and human subjects. Furthermore, NPs can provide artificial sensory feedback, allowing users to perceive the movements of prosthetic limbs and their haptic interaction with external objects. Recently, NP approach was used to build brain-nets that enable information exchange between individual brains and execution of cooperative tasks. This review focuses on invasive NPs for sensorimotor functions.
... To detect the learner's emotional state the computer requires some kind of interface. A good candidate is BrainComputer Interface (BCI) technology as it is a powerful tool used for communication between learning systems and users [22] [23] [24] [25]. ...
... Las herramientas que se toman en cuenta son algunas aplicaciones para una computadora fija. Algunos tienen como finalidad evaluar la BCI como un instrumento de control para las personas con alguna discapacidad motora (Kester, n.d.); (Pires et al., 2012); (Arango et al., 2013), evaluar el tiempo de entrenamiento necesario para utilizar cierto sistema (Lang, 2012) y la personalización del sistema dependiendo de las reacciones del usuario (Marchesi & Riccò, 2013). ...
... The index reflects visual processing and sustained attention [4] and is able to identify changes in attention related to external stimuli [4,12]. We built on top of prior research that utilize consumer EEG headsets which proved their success in detecting cognitive engagement in the learning domain [2,8,10,15,16] as well as in other domains [1,20,18]. We use the Neurosky Mindwave headset 1 (see Figure 1), a light-weight, dry-electrode EEG device. ...
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Obtaining information about audience engagement in presentations is a valuable asset for presenters in many domains. Prior literature mostly utilized explicit methods of collecting feedback which induce distractions, add workload on audience, and do not provide objective information to presenters. We present EngageMeter – a system that allows fine-grained information on audience engagement to be obtained implicitly from multiple brain-computer interfaces (BCI) and to be fed back to presenters for real time and post-hoc access. Through evaluation during an HCI conference (N audience =11, N presenters =3) we found that EngageMeter provides value to presenters (a) in real-time, since it allows reacting to current engagement scores by changing tone or adding pauses, and (b) post-hoc, since pre-senters can adjust their slides and embed extra elements. We discuss how EngageMeter can be used in collocated and distributed audience sensing as well as how it can aid presenters in long term use.
... For example, a bibliographic search in Google Scholar using the terms brain-machine interface and a closely related expression, brain-computer interface, yields more than 40 Since the introduction of experimental BMIs in the late 1990s, many applications have emerged for healthy subjects, outside the domains of basic and clinical research. BMIs for computer gaming (6,9,257,342,489,493,531,532,775,874), EEG-based that detect drowsiness in drivers (137,286,501,504,636,685,810), and even BMIs for education (371,526) are just a few examples of this parallel line of BMI development outside biomedical research. As a result, many started to consider BMIs as a method to augment human neural and physiological functions, such as cognitive abilities (242,518,519,539,888) and motor performance (148). ...
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Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain’s body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.
... BCI-based educational games have been used to reduce math anxiety (Verkijika and De Wet, 2015). Computer adaptive testing has been combined with real-time measurements of attention via EEG (Marchesi and Riccò, 2013). ...
... In fact, BCI devices, such as the Emotiv Epoch we used, allow freedom of movement (communicates with the server, using a bluethoot dongle) and reduce individuals' anxiety, thanks to wireless collection of the EEG signal and to the ergonomy of the device. BCIs process brainwaves into digital signals and it is used extensively in neuroscience [16,19], cognitive science [20,21,22,23] and cognitive psychology [24,25] research, as well as in games and education [26,27] and some other applications, such as attention training [28,29] music training and analysis [30,31]. ...
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Orthopaedic rehabilitation is a hot topic since the increasing age of the population in western countries implies that more and more people require invasive and inabilitating orthopaedic surgery, such as knee substitution, in order to recover physical functionality, autonomy and quality of life. Consequently, it's fundamental to test rehabilitation treatment able to increase efficacy and reliability of treatment, both with the aim to improve outcomes and decrease days of hospital stay. In this sense, Neuro-cognitive driven technology may have a great impact in this field. Starting by the paradigm of Action Observation Treatment, we have designed a pilot study using a 3D environment and wearable bio-sensors to boost rehabilitation in collaboration with an important orthopaedic hospital in Milan, Italy. We hypothized that the use of a 3D environment would decrease the time required for the recovery of motor functionality with respect to a 2D environment or to a standard treatment. We sampled 26 patients who accepted to participate in the study. They were randomly assigned to one of the three arms of the study. Preliminary data suggest that actually the Action Observation Treatment has the power to boos rehabilitation when matched with a traditional treatment. Furthermore, the 3D video stimulation seems to have a higher impact on cognitive and physical variables, thus suggesting that the use of 3D stimulation may constitute a cognitive tool to be used in hospital settings, while 2D videos might be considered an at-home tool to be used autonomously to maintain and further boost outcomes.
... The use of the NeuroSky sensor has been increasing for Human-Computer Interaction (HCI) and Brain-Computer Interface (BCI) research. Marchesi and Ricco proposed an e-learning system that customizes educational experience according to the attention and meditation signals captured via the NeuroSky sensor [16]. Al-Barrak and Kanjo used the same signals to distinguish relaxing outdoor places from boisterous places [3]. ...
Conference Paper
NeuroSky’s single-channel EEG sensor has drawn researchers’ interest because the sensor offers higher usability at a significantly lower cost. The sensor is minimally obtrusive, measuring the brainwaves from a single location on the head. This is an excellent feature from a usability standpoint. Yet, the sensor needs to be evaluated for specific applications. This paper presents preliminary assessment of the sensor in detecting drowsiness. A simulated driving task was used as a testbed. A total of 14 participants participated in the study. The results reveal no statistically significant difference in brain activities between the drowsy and the attentive states, indicating that the brainwaves used in the analysis are unable to distinguish the two driving states.
... Some examples include research to help patients control their prostheses and improve signal reception. Nevertheless, BCIs can be used in other areas [7][8][9][10]. For the purposes of this paper, device control, gaming and entertainment are areas of particular importance and thus are discussed in further detail below. ...
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... Some examples include research to help patients control their prostheses and improve signal reception. Nevertheless, BCIs can be used in other areas [7][8][9][10]. For the purposes of this paper, device control, gaming and entertainment are areas of particular importance and thus are discussed in further detail below. ...
... The cited study claims that compared with the control group, the students that took part in this project-based learning activity scored significantly better at the exams, despite the fact that the initial assessment showed that both groups had similar knowledge level, a fact that highlights the fact that learning in such a manner develops effective interdisciplinary competences. An interesting framework is proposed by Marchesi et al. [4], in which BCI becomes integrant part of the learning process: the described system monitors and tags the student attention and meditation levels observed from brain wave recordings, and correlates it with the lecture content, which might give insight to which part is more difficult; used as such, a BCI becomes a valuable feedback tool for teachers, as they get a clearer image of where additional explanation are needed and where the clarification might be shortened. A feature of this systems is the fact that it extends an existing e-learning platform and also works well with commercial BCI systems, which makes it particularly suitable for deployment on ultra-portable personal computers (tablets, smartphones, etc.). ...
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... BCIs are computational systems that permit interaction between users and the environment by means of their brain activity. This is a new way of communication in which users intentions are sending to external devices such as computers, mobiles, prostheses and wheelchairs, etc. [13] [14] [15] [16]. ...
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Three applications of computerized adaptive testing (CAT) to help solve problems encountered in educational settings are described and discussed. Each of these applications makes use of item response theory to select test questions from an item pool to estimate a student's achievement level and its precision. These estimates may then be used in conjunction with certain testing strategies to facilitate certain educational decisions. The three applications considered are (a) adaptive mastery testing for determining whether or not a student has mastered a particular content area, (b) adaptive grading for assigning grades to students, and (c) adaptive self-referenced testing for estimating change in a student's achievement level. Differences between currently used classroom procedures and these CAT procedures are discussed. For the adaptive mastery testing procedure, evidence from a series of studies comparing conventional and adaptive testing procedures is presented showing that the adaptive procedure results in more accurate mastery classifications than do conventional mastery tests, while using fewer test questions.
Conference Paper
In this workshop we study the research themes and the state-of-the-art of brain-computer interaction. Brain-computer interface research has seen much progress in the medical domain, for example for prosthesis control or as biofeedback therapy for the treatment of neurological disorders. Here, however, we look at brain-computer interaction especially as it applies to research in Human-Computer Interaction (HCI). Through this workshop and continuing discussions, we aim to define research approaches and applications that apply to disabled and able-bodied users across a variety of real-world usage scenarios. Entertainment and game design is one of the application areas that will be considered.
Brain-computer interfaces for hci and games, CHI '08 Extended Abstracts on Human Factors in Computing Systems
  • Anton Nijholt
  • Desney Tan
  • Brendan Allison
  • R Milan
  • Bernhard Graimann