A New Single Trial P300 Classification Method

A New Single Trial P300 Classification Method

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) and Emanuel Donchin (Department of Psychology, University of South Florida, Tampa, FL, USA)
Copyright: © 2012 |Pages: 11
DOI: 10.4018/jehmc.2012100103


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.
Article Preview

Different methods of signal classification have been tested with the P300-BCI Speller including Stepwise Linear Discriminant Analysis (SWLDA) (Krusienski, Sellers, McFarland, Vaughan, & Wolpaw, 2008), Support Vector Machine (SVM) (Kaper, Meinicke, Grossekathoefer, Lingner, & Ritter, 2004), Independent Component Analysis (ICA) (Li, Sankar, Arbel, & Donchin, 2009), Matched Filter (Serby, Yom-Tov, & Inbar, (2005), and Wavelet Analysis (Bostanov, 2004). Krusienski, Sellers, Cabestaing, Bayoudh, McFarland, Vaughan, and Wolpaw (2006) investigated the techniques mentioned above and concluded that SWLDA was the most accurate and practical processing method for P300 speller. However, SWLDA and other techniques share the same drawback. They need to average several trials to remove the background noises and enhance the magnitude of P300 response before applying the P300 classifier on EEG signal. This need for averaging slows the communication process of the P300 BCI. It would clearly be advantageous to employ a method that would allow reliable detection of P300 in a single trial. Our goal in the present study is to develop an algorithm for the single trial detection of P300 response.

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 10: 4 Issues (2019): Forthcoming, Available for Pre-Order
Volume 9: 4 Issues (2018): 2 Released, 2 Forthcoming
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing