Classification in emotional BCI using phase information from the EEG

Synchronization and distributed functional networks have been used with success in different areas of engineering. In this paper we use the synchronization information from electroencephalogram (EEG) channels through the Phase Locking Value (PLV) as a potential classification method for a Brain Computer Interface (BCI); this achieved using emotional schematic faces as stimuli in a motor imagery (MI) task. Based on the variations of the PLV values for each proposed task and for each participant, the principal channel pairs are identified. Selected channel pairs, corresponding with the accomplished task, present PLV patterns similarly to Evoked Potentials (ERS/ERD) which are widely used as classification features for MI based BCI.