Graph model of phase lag index for connectivity analysis in EEG of emotions

Emotion recognition is useful in several fields, starting from medical diagnosis to driving a Brain-Computer Interface (BCI) or helping people with disabilities. During the last decades, many researchers applied automatic strategies to identify emotional states based on data acquired by electroen-cephalography (EEG). However, the task is very hard and results have been often ambiguous. This work aims to perform brain connectivity studies of EEG data of four self-stimulated emotional classes ('relax', 'anger', 'happiness', 'sadness') using a graph model of the Phase Lag Index (PLI), being PLI a measurement of connection insensitive to volume conduction effect. Qualitative results show that, for the analyzed emotions, connectivity analysis indicates some relevant differences both in the active brain regions and in the bandwidths involved in the activation. This method for connectome generation and analysis shows that useful information can be derived and used for contributing to disambiguating the problem of automatic emotion recognition.