Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment

Abstract

Electroencephalogram (EEG) signals are sensitive to the level of Mental Workload (MW). However, the random non-stationarity of EEG signals will lead to low accuracy and a poor generalization ability for cross-session MW classification. To solve this problem of the different marginal distribution of EEG signals in different time periods, an MW classification method based on EEG Cross-Session Subspace Alignment (CSSA) is presented to identify the level of MW induced in visual manipulation tasks. The Independent Component Analysis (ICA) method is used to obtain the Independent Components (ICs) of labeled and unlabeled EEG signals. The energy features of ICs are extracted as source domains and target domains, respectively. The marginal distributions of source subspace base vectors are aligned with the target subspace base vectors based on the linear mapping. The Kullback–Leibler (KL) divergences between the two domains are calculated to select approximately similar transformed base vectors of source subspace. The energy features in all selected vectors are trained to build a new classifier using the Support Vector Machine (SVM). Then it can realize MW classification using the cross-session EEG signals, and has good classification accuracy.