Electrode subset selection methods for an EEG-based P300 brain-computer interface.

PURPOSE: An electroencephalography (EEG)-based P300 speller is a type of brain-computer interface (BCI) that uses EEG to allow a user to select characters without physical movement. In general, using fewer electrodes for such a system makes it easier to set up and less expensive. This study addresses the question of electrode selection for EEG-based P300 systems.
METHODS: Data from 13 subjects collected with a 16-electrode cap was analyzed. The optimal subsets of electrodes of sizes 1-15 were calculated for each subject and for the group as a whole. The methods of exhaustive search, forward selection, and backward elimination were then compared to each other and to these optimal subsets.
RESULTS: The results show that, while none of the methods consistently picked the best-performing electrode subsets, all methods were able to find small electrode subsets that provided acceptable accuracy both for individuals and for the whole group. The computationally intensive exhaustive search method provided no statistically significant increase in performance over the much quicker forward and backward selection methods.
CONCLUSIONS: The forward and backward selection methods are preferred for electrode selection. IMPLICATIONS FOR REHABILITATION: A P300 speller is a type of brain-computer interface that allows a user to select characters without physical movement. Using fewer electrodes reduces setup time and cost for an EEG-based P300 speller. We show that acceptable P300 speller performance can be achieved with as few as four electrodes. We compare methods of selecting electrode sets and identify fast and efficient methods for customizing electrode sets for individuals.