An improved P300-based brain-computer interface

A brain-computer interface (BCI) is a system for direct communication between brain and computer. The BCI developed in this work is based on a BCI described by Farwell and Donchin in 1988, which allows a subject to communicate one of 36 symbols presented on a 6 /spl times/ 6 matrix. The system exploits the P300 component of event-related brain potentials (ERP) as a medium for communication. The processing methods distinguish this work from Donchin's work. In this work, independent component analysis (ICA) was used to separate the P300 source from the background noise. A matched filter was used together with averaging and threshold techniques for detecting the existence of P300s. The processing method was evaluated offline on data recorded from six healthy subjects. The method achieved a communication rate of 5.45 symbols/min with an accuracy of 92.1% compared to 4.8 symbols/min with an accuracy of 90% in Donchin's work. The online interface was tested with the same six subjects. The average communication rate achieved was 4.5 symbols/min with an accuracy of 79.5% as apposed to the 4.8 symbols/min with an accuracy of 56% in Donchin's work. The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.

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

[2]  D. H. Lange,et al.  Modeling and estimation of single evoked brain potential components , 1997, IEEE Transactions on Biomedical Engineering.

[3]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[4]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[6]  G. Pfurtscheller,et al.  Graz-BCI: state of the art and clinical applications , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  P R Kennedy,et al.  Direct control of a computer from the human central nervous system. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  M. Stokes,et al.  Probabilistic methods in BCI research , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Simon Haykin,et al.  Communication Systems , 1978 .

[10]  F Babiloni,et al.  Linear classification of low-resolution EEG patterns produced by imagined hand movements. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  Helge J. Ritter,et al.  Improving Transfer Rates in Brain Computer Interfacing: A Case Study , 2002, NIPS.

[12]  M J Stokes,et al.  EEG-based communication: a pattern recognition approach. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[13]  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.

[14]  H. Timothy Bunnell,et al.  Toward a P300-based Computer Interface , 2002 .

[15]  William Z Rymer,et al.  Guest Editorial Brain–Computer Interface Technology: A Review of the Second International Meeting , 2001 .

[16]  Elad Yom-Tov,et al.  Movement-related potentials during the performance of a motor task I: The effect of learning and force , 2001, Biological Cybernetics.

[17]  P.R. Kennedy,et al.  A decision tree for brain-computer interface devices , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[19]  D.J. McFarland,et al.  The Wadsworth Center brain-computer interface (BCI) research and development program , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  W. A. Sarnacki,et al.  Brain–computer interface (BCI) operation: optimizing information transfer rates , 2003, Biological Psychology.

[21]  T. Sejnowski,et al.  Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects , 2000, Clinical Neurophysiology.

[22]  Bernhard Graimann,et al.  Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis , 2004, IEEE Transactions on Biomedical Engineering.

[23]  F. Cincotti,et al.  The use of EEG modifications due to motor imagery for brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[25]  Jessica D. Bayliss,et al.  A Flexible Brain-Computer Interface , 2001 .

[26]  B.Z. Allison,et al.  ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  G Pfurtscheller,et al.  Current trends in Graz Brain-Computer Interface (BCI) research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[28]  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.

[29]  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.

[30]  H. Ritter,et al.  Generalizing to new subjects in brain-computer interfacing , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.

[32]  T. Sejnowski,et al.  Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention , 1999, The Journal of Neuroscience.

[33]  A. Schwartz,et al.  Work toward real-time control of a cortical neural prothesis. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[34]  J D Bayliss,et al.  A virtual reality testbed for brain-computer interface research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[35]  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.

[36]  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.

[37]  Gideon F. Inbar,et al.  Movement-related potentials during the performance of a motor task II: Cerebral areas activated during learning of the task , 2001, Biological Cybernetics.

[38]  S P Levine,et al.  A direct brain interface based on event-related potentials. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[39]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[40]  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.