EEG decoding for musical emotion with functional connectivity features

Emotion refers to the subjective emotional experience that humans generate in specific situations, typically accompanied by physiological and psychological changes. In the field of emotions, multi-channel EEG emotional features can better reflect the collaborative mechanisms across multiple brain regions. Therefore, we propose a novel feature called asPLV (averaged sub-frequency phase locking value) based on the Morlet transform method to construct functional network edges. The proposed feature encompasses a comprehensive analysis of phase synchronization across sub-frequency bands spanning a wide range of frequencies and has the potential to reduce fluctuations arising from reliance on a single frequency band. We designed a music-evoked emotion experiment aimed at inducing corresponding emotions in participants while simultaneously recording their electroencephalogram (EEG) signals and extracted our proposed asPLV feature for classification. The results show that the proposed feature displays superior classification performance and generalization compared to other state-of-theart methods. The proposed method is not only effective in successfully distinguishing between different emotions but also introduces a novel brain network metric to elucidate the collaboration and information exchange among emotion-related brain regions. Moreover, the asPLV feature could provide new insights for the development of an emotional brain-computer interface (BCI).