Assessment of event-related potential of independent components for intended direction classification

Electroencephalography (EEG) signals do not show electrical neuronal activity at source level. Thus, in EEG-based brain machine interface, information relatively easy to occur, such as motor imagery, has been used. Independent component analysis is a method to estimate multiple sources from EEG signals. If independent components can take some parts encoded by movement direction, it could improve classification performance for brain-machine interface. Therefore, this study aimed to find features contributing to specific direction in planning phase from independent components. Subjects performed a reaching task and were instructed to move their hand to one of five directions. Independent components were calculated from EEG signals during planning phase. We constructed feature vectors of each independent component with analysis of variance (ANOVA) and performed direction classification. Our results showed that using event-related potentials of independent components, classification accuracy were higher than chance level. Independent components that achieved high performance were associated with local region in the brain. Times and duration contributing to high performance were not identical to one another. We confirmed that independent components related to intended direction can be extracted and contributed to high accuracy.