Multi-task Feature Learning for EEG-based Emotion Recognition Using Group Nonnegative Matrix Factorization

Electroencephalographic sensors have proven to be promising for emotion recognition. Our study focuses on the recognition of valence and arousal levels using such sensors. Usually, ad hoc features are extracted for such recognition tasks. In this paper, we rely on automatic feature learning techniques instead. Our main contribution is the use of Group Nonnegative Matrix Factorization in a multi-task fashion, where we exploit both valence and arousal labels to control valence-related and arousal-related feature learning. Applying this method on HCI MAHNOB and EMOEEG, two databases where emotions are elicited by means of audiovisual stimuli and performing binary inter-session classification of valence labels, we obtain significant improvement of valence classification F1 scores in comparison to baseline frequency-band power features computed on predefined frequency bands. The valence classification F1 score is improved from 0.56 to 0.69 in the case of HCI MAHNOB, and from 0.56 to 0.59 in the case of EMOEEG.