Study on an effective cross-stimulus emotion recognition model using EEGs based on feature selection and support vector machine

Abstract

Electroencephalographic (EEG)-based emotion recognition has received increasing attention in the field of human–computer interaction (HCI) recently, there however remain a number of challenges in building a generalized emotion recognition model, one of which includes the difficulty of an EEG-based emotion classifier trained on a specific stimulus to handle other stimuli. Little attention has been paid to this issue. The current paper is to study this issue and determine the feasibility of coping with this challenge using feature selection. 12 healthy volunteers were emotionally elicited when watching the video clip. Power spectral density (PSD) and brain asymmetry (BAY) were extracted as EEG features. Support vector machine (SVM) classifier was then examined under within-stimulus conditions (samples extracted from one video were sent to both training set and testing set) and cross-stimulus conditions (samples extracted from one video were merely sent to one set, training set or testing set alternatively). The within-stimulus 5-class classification performed fairly well (accuracy: 93.31 % for PSD and 85.39 % for BAY). Cross-stimulus classification, however, deteriorated to low levels (46.22 % and 46.2 % accordingly). Trained and tested with the most robust feature subset selected by SVM-recursive feature elimination (RFE), the mean 5-class performance of cross-stimulus classifier was significantly improved to 68.89 and 64.44 % for PSD and BAY respectively. These results suggest that cross-stimulus emotion recognition is reasonable and feasible with proper methods and brings EEG-based emotion recognition models closer to being able to discriminate emotion states in practical application.