A comparison between a matrix-based and a region-based P300 speller paradigms for brain-computer interface

A brain-computer interface (BCI) is a system that conveys messages and commands directly from the human brain to a computer. The BCI system described in this work is based on P300 wave. The P300 is a positive peak of an event-related potential (ERP) that happens 300 ms after a stimulus. One of the most well-known and widely-used P300 applications is P300 speller designed by Farwell-Donchin in 1988. The Farwell-Donchin paradigm has been a benchmark in P300 BCI. In this paradigm, a 6x6 matrix of letters and numbers is displayed and subject focuses on a target character while rows and columns of characters flash. By detecting P300 for one row and one column, the target character can be identified. In this paper, it is shown that there is a human perceptual error in Farwell-Donchin paradigm. To remove this error, a new region-based paradigm is presented. Using experimental results, it is shown that the new paradigm has several advantages and it achieves a better accuracy compared to the Farwell-Donchin paradigm.

[1]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[2]  K L Shapiro,et al.  Temporary suppression of visual processing in an RSVP task: an attentional blink? . , 1992, Journal of experimental psychology. Human perception and performance.

[3]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[4]  Gert Pfurtscheller,et al.  Brain-computer interface: a new communication device for handicapped persons , 1993 .

[5]  R. Fazel-Rezai,et al.  Human Error in P300 Speller Paradigm for Brain-Computer Interface , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  E. Donchin,et al.  The mental prosthesis: Assessing the speed of a brain-computer interface , 1998 .

[7]  R. Fazel-Rezai,et al.  P300 wave feature extraction: preliminary results , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[8]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

[9]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[10]  Reza Fazel-Rezai,et al.  Brain Signals: Feature Extraction and Classification Using Rough Set Methods , 2005, RSFDGrC.

[11]  J. Polich,et al.  On P300 measurement stability: habituation, intra-trial block variation, and ultradian rhythms , 1999, Biological Psychology.

[12]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[13]  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.

[14]  A Kostov,et al.  Parallel man-machine training in development of EEG-based cursor control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[15]  Reza Fazel-Rezai,et al.  P300 Wave Detection Based on Rough Sets , 2006, Trans. Rough Sets.

[16]  E R John,et al.  Information Delivery and the Sensory Evoked Potential , 1967, Science.

[17]  N. Kanwisher.  Repetition blindness: Type recognition without token individuation , 1987, Cognition.