EEG source localization by memory network analysis of subjects engaged in perceiving emotions from facial expressions

The memory network is a result of current dipoles created in the brain. Localizing the source of these current flows is known as source localization, and it could potentially reveal which parts of the brain are actually responsible for a particular brain activity. It would also increase the spatial resolution of an EEG recording by identifying the true source of multiple correlated readings. In our experiments, we employed memory networks to classify perception of emotional instances conveyed in facial expressions as well as to localize sources. These networks were created by selectively evaluating EEG channel signals pairwise for Granger causality. Channel selection was based on clustering of EEG features by Self Organizing Feature Map (SOFM). Principal Component Analysis (PCA) was employed for dimension reduction and noise elimination of EEG features. Finally a new metric based on Fischer's discriminant was used to compare different source localization techniques, where real source locations are unknown. The perception of the stimuli was classified as belonging to one the following classes i) Happy ii) Sad iii) Fear iv) Relaxed. The created memory networks could classify perception of emotional content in 90.64% of cases. Comparison by the proposed Fischer Discriminant based metric revealed that the proposed network identification technique performs better at source localization as compared to independent component based source localization.