Frequency peak features for low-channel classification in motor imagery paradigms

The expansion of brain-computer interfaces (BCIs) to outside the research laboratory has historically been hampered by their difficulty of use. Well-functioning BCIs often require many channels, which can be difficult to properly prepare and require expert support. Low-channel setups, however, can lead to poor or unreliable classification of intent. Here we introduce a novel method for extracting more information from a single EEG channel and test it on a ten subject motor imagery dataset. Instead of looking at bandpower or phase synchrony, we test the average frequency within each trial to see if there are task-dependent changes in the spectral locations of neural frequency peaks. We show that using this feature in combination with standard bandpower features is significantly better than bandpower features alone across subjects, both for standard electrodes and electrodes that include a Laplacian filter.