Assessment of new spectral features for eeg-based emotion recognition

The choice of appropriate features for automatic emotion recognition based on electroencephalographic (EEG) signals remains to date an open research question. In this paper we explore a wide range of potentially useful features, including original ones, comparing them to previous proposals through a rigorous experimental evaluation, using a strict cross-validation protocol. In particular we assess the effectiveness of new spectral features-both in multi-channel and single-channel EEG setups-for the problem of discriminating positively and negatively excited emotions. The evaluation is conducted using the ENTERFACE´06 dataset allowing us to study the behaviour of the tested features across different subjects. Our results prove the usefulness of various new spectral features even in single-channel setups. We also observe that the optimal selection of features is highly subject-dependent. Finally combining different groups of features we find the valence recognition accuracy to be possibly as high as 78%.