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

Abstract

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.

• Brain-computer interface
• event-related potentials
• P300 speller
• channel selection

Acknowledgements

The authors would like to thank the attendees of the Fourth International BCI Meeting for their insightful poster comments, which were of great help while preparing this manuscript.

Declaration of interest

The project described was supported by Grant Number R21HD054913 from the National Institute of Child Health And Human Development (NICHD) in the National Institutes of Health (NIH). Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of NICHD or NIH. The authors report no conflicts of interest.

Notes

¹Ties in training accuracy were broken by comparing accuracy at a reduced number of sequences (flashes). In the cases where a tie remained, the tie was broken randomly.