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Introduction
As demonstrated by neuroscience scientists and educators, portable EEG headsets could be used to capture the brainwaves (electroencephalograms, EEG). The more engaged the students were during the class, the higher their brainwaves synced up. Evidence has shown the brainwaves of students in class could be used to evaluate the attractiveness of the lectures and provide an objective index for teachers to improve the teaching and for students to revise the lectures.
In this project, (1) the synchronized brainwaves of students would be captured using a portable headset to encourage the engagement of students in class. (2) The alpha brainwaves (frequency range 8-12 Hz) will be used to reflect the cognitive fatigue/attention of individual students during lectures, the change of alpha waves accompanied with the lecture recordings will offer a guideline for students on after-lecture review purposes. (3) The synchronization of brain waves across students in class will be calculated to indicate the students’ engagement during lectures and provide a quantitative assessment method for teachers to improve the outcome of lectures.
Method
Whether the brainwaves could be used to improve teaching and learning in higher education is still an open-ending question. One recent study conducted by New York University hooked up the students of 12 students in a biology class and acquired their brainwaves over the course of one semester using a portable EEG device. Their work has been published in a top-leading journal Current Biology reported that brain-to-brain synchrony using EEG signals could be used to track the students’ interactions in class [1], the more engaged the students, the higher the synchrony of their brain activities. Recent studies conducted by psychologists at Tsinghua University also demonstrated that inter-brain brainwave coupling could reveal learning-related attention in primary school [2] and high school [3] using portable EEG headsets. Specifically, they recorded the EEG of high school students throughout one semester and revealed students with a better academic performance in the Math course showed a higher inter-brain synchronization, while students with higher exam scores in the Chinese course showed higher inter-brain couplings with the top students only. The above-mentioned evidence demonstrated the feasibility of using a portable EEG headsets to measure brainwaves in class, as well as assess the engagement of students using the EEG features.
In this project, we aim to build up a BCI platform to enhance teaching and learning in biomedical engineering based on portal EEG signals and lecture recordings. The paradigm of the platform is illustrated below (Figure 1).
Figure 1. The workflow of BCI in Classroom. In-class engagement and interactions could be assessed via BCI classroom.
In class, EEG activities of students during lectures would be recorded by using a 5-channel EMOTIV Insight 2.0 Mobile Brainwear. To synchronize the brainwaves across students, a steady state visually evoked potentials (SSVEP) paradigm would be used at the beginning of lecture notes to align the EEG across students. At the same time, lecture recordings (zoom or other audio/visual systems) would also be aligned to the brainwaves and provide feedback to both teachers and students after lectures.
In the platform, the EEG signals will be pre-processed, and band-pass filtered. The power spectrum in the theta (4-8 Hz), alpha (8-12 Hz), low beta (12-18 Hz), and high beta (18-30 Hz) frequency bands will be estimated and used to indicate the features related to cognitive fatigue and attention. In addition, the instantaneous phase of different frequency brainwaves will be estimated using Hilbert transformation, and the time-varying brain-to-brain phase synchronization will be calculated at two different levels: (1) group synchrony, which is used to track the overall engagement and provide feedback to teachers (2) individual-to-group synchrony, it could provide the contributions of peer interactions as we conducted in previous neuroimaging studies [4-6].
The output of the platform would provide time intervals as well as lecture recording episodes in which students showed the highest and lowest engagement to teachers for teaching development. The cognitive fatigue and engagement indexes of individual students along with the lecture recordings will be provided to individual students for revising purposes. A report (Figure 2) will be generated and sent to them separately.
Figure 2. The proposed report to teachers and students for improvement. (a) The report to the teacher indicates the overall engagement of students. (b) The report to students to indicate the engagement and attention in class.
Expected Results
Our BCI Classroom will provide two versions of the report, i.e., the student version and the teacher version. For anyone who participates in the BCI Classroom, a report will be generated and sent a softcopy to them, which has been done in the science museum workshop to the participants, and the students can monitor their performance and enhance their interest. In the teacher report, group synchronization across students will be offered, as implemented in the NYU group, the higher the synchronization, the more engaged of students. The portion of lower-engaged lectures may be improved. In the student report, attention and engagement (individual-to-group synchronization) will be provided, and the episodes of lower engagement/attention will be suggested to be revisited.
References:
[1] S. Dikker et al., "Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom", Curr Biol. 2017, 27(9):1375-1380.
[2] J. Chen, B. Xu, D. Zhang, "Inter-brain coupling analysis reveals learning-related attention of primary school students", Educ Technol Res Dev., 2023, doi: 10.1007/s11423-023-10311-3.
[3] J. Chen, P. Qian, X. Gao, B. Li, Y. Zhang, D. Zhang, "Inter-brain coupling reflects disciplinary differences in real-world classroom learning", NPJ Sci Learn. 2023, 8(1):11.
[4] W. W. Peng, W. Lou, X. X. Huang, Q. Ye, R. K. Y. Tong, and F. Cui, "Suffer together, bond together: Brain-to-brain synchronization and mutual affective empathy when sharing painful experiences", Neuroimage. 2021, 238:118249.
[5] X. Li1, W. Lou1, W. Zhang, R. K. Y. Tong, L. Hu, and W. Peng, "Ongoing first-hand pain facilitates somatosensory resonance but inhibits affective sharing in empathy for pain," Neuroimage. 2022, 263:119599.
[6] W. Lou, X. Li, R. Jin*, and W. Peng, "Time-Varying Phase Synchronization of Resting-State fMRI Reveals a Shift Toward Self-referential Processes During Sustained Pain", Pain. 2024, 165(7):1493-1504.
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