Megrez: MOOC-Oriented EEG-Based Arousal of Brain Detection and Adjustment Scheme

The Massive Open Online Course (MOOC) is based on the Connectionism Theory and the Open Pedagogy of Network Learning, realizing the large-scale open sharing of high-quality education resources via the Internet. MOOC is conducive to the multi-dimension and personalized teaching. However, compared with the mode of traditional classroom teaching, there are also insurmountable drawbacks of MOOC to be addressed. Especially, it is difficult for educators to know the true states of students in real-time when they are learning online, and control the quality of teaching and learning. In this paper, we carried on the thorough research to the arousal degree of learner's brain, and proposed the MOOC-oriented EEG-based arousal of brain detection and adjustment scheme, and constructed the prototype system named Megrez. Megrez can analyze the brain arousal with the EEG data of learners wearing portable EEG detection devices. The energy value of β wave of EEG is selected as the main feature of brain arousal. After preprocessing the EEG data, the SVM classifier is trained, which classification performance is proved satisfactory. When a learner's brain is in a low arousal state, Megrez automatically plays the appropriate music to improve the arousal of brain. The experimental results show that Megrez can effectively detect the arousal of learner's brain, help learners avoid inefficient learning in the negative state, and can also adjust the state of the brain if necessary.