AttentivU: an EEG-based closed-loop biofeedback system for real-time monitoring and improvement of engagement for personalized learning
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sensors-19-05200.pdf
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Author(s)
Kosmyna, Nataliya
Maes, Patricia
Date Issued
November 27, 2019
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Kosmyna, Nataliya, and Patricia Maes, "AttentivU: an EEG-based closed-loop biofeedback system for real-time monitoring and improvement of engagement for personalized learning." Sensors 19, 23 (Nov. 2019): no. 5200 doi 10.3390/s19235200 ©2019 Author(s)
Version
Final published version
Abstract
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. Keywords: electroencephalography (EEG); feedback; closed loop; real-time; brain–computer interfaces
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/s19235200
