Chapter 4 - Deep learning-based cognitive state prediction analysis using brain wave signal

Abstract

Teaching learning process is seeing a large transformation in this digital age. It involves digital classrooms with various accessories of online tools such as video-conferencing, digital materials, and other platforms for learning and assessment with options for both real-time and self-paced work in addition to the availability of teachers over video-conferencing, text, phone, email, etc. Traditional classrooms are transforming into smart classrooms with the inclusion of the latest technologies and offering an interactive learning environment for students. Online tools such as video-conferencing, multimedia lessons, digital materials, and e-learning platforms with options for both real-time learning and self-paced learning provide a pleasant and immersive experience. In addition to these features, assessment of cognitive state during the learning phase has been proven to improve the efficiency of online learning. Here, we focus toward the analysis of cognitive states over diverse learning assignments using electroencephalogram (EEG) signals that were obtained by means of 128 channels emotive epoch headset device. The artifacts present in original signals were filtered by using linear filtering. So as to determine the exact concentration levels, the fuzzy fractal dimension measures and the discrete wavelet transform were adapted to the same extracted EEG signals for extracting he features. These extracted parameters were then classified into concentration levels using the prominent deep learning methodology, the enhanced convolutional neural network (ECNN), which was proven to be of higher accuracy compared to other classifiers. ECNN can then be used to control cognitive states as a feedback mechanism.