Neural component analysis: source localisation for motor imagery classification

The electroencephalogram (EEG) records a summed mixture of multiple sources of neural activity distributed throughout the brain. Source separation methods aim to un-mix the EEG in order to recover activity generated by the original sources. However, most current state-of-the-art source separation methods do not take into account the physical locations of sources of EEG activity. We present a new source separation method which uses an accurate model of the head to un-mix the EEG into individual sources based on their physical locations. We apply our method to an EEG dataset recorded during motor imagery and show that it is able to identify sources that are located in distinct physical regions of the brain. We compare our method to independent component analysis and show that our sources have higher spatial specificity and, furthermore, allow higher classification accuracies (a mean improvement in accuracy of 8.6% was achieved p =0.039).