Deep Learning Based EEG Analysis Using Video Analytics

Electrical signals generated in the brain, known as Electroen-cephalographic (EEG) signals, are used in the study of the brain states spanning from normal wakefulness all the way to critical conditions such as seizure. This work aims to classify distinct EEG categories using EEG video representations with deep learning. EEG videos are utilized to obtain spatial, spectral and temporal features from EEG. The study utilizes Delaunay Triangulation interpolation to obtain EEG images from raw signals and further EEG videos are generated. EEG video features are given to CNN+LSTM network. Feature Pyramid Network (FPN) has also been adapted for EEG video classification. The results are presented here on two different classification scenarios that capture a range of cognitive activities: (1) EEG baselines (109 subjects) and (2) Mental Arithmetic vs Rest (36 subjects). EEG video-based analyses result in mean accuracies of 92.5% and 98.81% for the two different datasets with the improvement of 3.27% and 1.31% respectively over the state-of-the-art.