Emotion Recognition and EEG Analysis Using ADMM-Based Sparse Group Lasso

Abstract

This study presents an efficient sparse learning-based pattern recognition framework to recognize the discrete states of three emotions—happy, angry, and neutral emotion—using electroencephalogram (EEG) signals. In affective computing with massive spatiotemporal brainwave signals, a large number of features can be extracted to capture various information from multivariate brain data. However, it is often a challenge to model high-dimensional features efficiently in consideration of the intrinsic structure, such as channel location, feature group, time epoch, etc. In this study, features were extensively extracted from EEG signals and were applied on a structured sparse learning model to perform feature selection and classification simultaneously. An efficient ADMM-based algorithm with a closed-form solution was developed to solve the sparse group model. Experimental results show that the proposed method is capable of selecting a small number of important neural features to discriminate the three emotion states with high classification accuracy. With greatly enhanced interpretability and efficiency to learn neural signatures of brain activity from high-dimensional-feature, low-sample-size brain imaging data, the presented computational framework is promising for handling emotion recognition problems with high-dimensional brain imaging data.