Work

Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers

Year: 2013

Type: article

Abstract: 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.

Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering

DOI: 10.1109/tnsre.2013.2253125

Authors: Chandra S. Throckmorton, Kenneth A. Colwell, David B. Ryan, Eric W. Sellers, Leslie M. Collins

Institutions: Duke University, East Tennessee State University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: Association for Psychological Science, National Institute on Deafness and Other Communication Disorders, Lawrence Livermore National Laboratory

Awards: Towards Clinical Acceptability: Enhancing the P300-based Brain-Computer Interface, G51663055