Real Time Cognitive State Prediction Analysis using Brain Wave Signal

Abstract

The teaching-learning process is seeing a big 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. To improve the online learning efficiency, assessing the cognitive state during the learning phase is highly required for the success of these developments. This work focused on cognitive state analysis during different learning tasks is determined by EEG brain signals that are captured using 128 channels Emotive Epoch headset device. Artifacts prominent in raw signals are filtered by linear filtering. Feature extraction for determination of concentration levels is done by applying fuzzy fractal dimension measures and Discrete Wavelet Transform (DWT) on the processed signals. The classification of extracted parameters into concentration levels is done by using deep learning algorithms like Enhanced Convolutional Neural Network (ECNN). This ECNN deep learning classification is highly accurate amongst all other remaining classifiers and is used as a feedback model to regulate this cognitive state.

State-of-the-art mental tasks classification based on electroencephalograms: a review

“…More recently, an end-to-end role assigned CNN was proposed inLee et al (2020) to classify nine arm movements, after selecting 24 EEG channels including frontal, central, and parietal locations. InSophia et al (2021), an enhanced CNN was implemented for classification of different MTs with a high accuracy of 98% with prior feature extraction from the α band. Furthermore, inSuchetha et al (2021), a sequential and multibranch CNN with prior feature extraction was proposed for classification of four MTs and one baseline task; however, there are limitations posed by EEG noises.…”