A New Single Trial P300 Classification Method

Kun Li, Ravi Sankar, Ke Cao, Yael Arbel and Emanuel Donchin
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Kun Li: Department of Electrical Engineering, University of South Florida, Tampa, FL, USA
Ravi Sankar: Department of Electrical Engineering, University of South Florida, Tampa, FL, USA
Ke Cao: H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
Yael Arbel: Department of Communication Sciences and Disorders, University of South Florida, Tampa, FL, USA
Emanuel Donchin: Department of Psychology, University of South Florida, Tampa, FL, USA

International Journal of E-Health and Medical Communications (IJEHMC), 2012, vol. 3, issue 4, 31-41

Abstract: P300-Speller is one of the most practical and widely used Brain Computer Interface (BCI) for locked-in people who are not able to communicate with others via traditional communication methods. Many signal processing techniques have been utilized in P300-Speller to restore the communication ability of these locked-in people. These techniques are capable of achieving high classification accuracy. However the classification accuracy dramatically decreases for single trial analysis. The reason for that is that the noises existing in the recorded signals are usually removed by averaging several trials. When only a single trial is available, averaging is no longer an option for de-noising. The “averaging” step becomes the bottle neck of P300 response detection which highly limits the processing speed. Researchers are looking for techniques that can accomplish the classification task in a single trial. In this work, a new, effective but simple processing technique for single trial electroencephalography (EEG) classification using variance analysis based method is presented. This method achieved an overall accuracy of 84.8% for single trial P300 response identification. When compared with a single trial stepwise linear discriminant analysis (SWLDA), the authors’ method in terms of overall accuracy is more accurate and the data communication speed is significantly improved.

Date: 2012
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