Enhancing the Self-Regulated Learning Process Using a Brain-Computer Interface with Auditory Neurofeedback

While self-regulated learning takes a significant role in the learning process, students struggle with weak performance monitoring and a lack of external aids. Implementation of the Brain-Computer Interface can assist the students in quantitatively measuring their engagement or concentration during self-regulated learning, hence enhancing the self-regulated learning experience by providing real-time feedback for real-time monitoring of performance and correction. In this project, the level of concentration or engagement of the students will be monitored by implementation of a neuro-physiological computing system and calculation of engagement index from alpha, beta, and theta bands of EEG signals. A real-time feedback based on the aforementioned index in the form of audio, background music with dynamic volume will be developed and evaluated.