Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers
Document Type
Article
Publication Date
5-22-2013
Description
P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate.
Citation Information
Throckmorton, Chandra S.; Colwell, Kenneth A.; Ryan, David B.; Sellers, Eric W.; and Collins, Leslie M.. 2013. Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers. IEEE Transactions on Neural Systems and Rehabilitation Engineering. Vol.21(3). 508-517. https://doi.org/10.1109/TNSRE.2013.2253125 PMID: 23529202 ISSN: 1534-4320