Plug-and-Play EEG-Based Student Confusion Classification in Massive Online Open Courses

Abstract

Use of Electroencephalography (EEG)-based monitoring devices in classrooms has seen greater uptake with increased interest in Internet of Things (IOT) and human-computer interaction (HCI). The ability to interact directly with digital interfaces using brain signals offer significant advantage towards seamless and natural communication by simply thinking of the desired outcome. We propose the implementation of a new leave-one-subject-and-video-out paradigm alongside a plug-and-play lightweight EEG-based classification framework to accurately analyse the efficacy of EEG signals in determining students’ confusion levels. The proposed methodology achieves state-of-the-art performance, reaching 95.75% classification accuracy.