An application of feature selection to on-line P300 detection in brain-computer interface

We propose a new EEG-based wireless brain computer interface (BCI) with which subjects can “mind-type” text on a computer screen. The application is based on detecting P300 event-related potentials in EEG signals recorded on the scalp of the subject. The BCI uses a linear classifier which takes as input a set of simple amplitude-based features that are optimally selected using the Group Method of Data Handling (GMDH) feature selection procedure. The accuracy of the presented system is comparable to the state-of-the-art systems for on-line P300 detection, but with the additional benefit that its much simpler design supports a power-efficient on-chip implementation.

[1]  G. R. Muller,et al.  Brain oscillations control hand orthosis in a tetraplegic , 2000, Neuroscience Letters.

[2]  G.F. Inbar,et al.  An improved P300-based brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[4]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  R. Andersen,et al.  Cognitive neural prosthetics. , 2010, Annual review of psychology.

[6]  Refet Firat Yazicioglu,et al.  Ultra-low power biopotential interfaces and their application in wearable and implantable systems , 2007, 2007 2nd International Workshop on Advances in Sensors and Interface.

[7]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[9]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[11]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[12]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[13]  J. Wolpaw,et al.  Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.

[14]  W. Pritchard.  Psychophysiology of P300. , 1981, Psychological bulletin.

[15]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[16]  R. Andersen,et al.  Cognitive neural prosthetics. , 2010, Annual review of psychology.

[17]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[18]  Fusheng Yang,et al.  BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications , 2004, IEEE Transactions on Biomedical Engineering.

[19]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.