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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%.