Suboptimal sensor subset evaluation in a P300 brain-computer interface

A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain activity. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. This paper deals with the choice of a reduced set of sensors for the P300 speller. A low number of sensors allows decreasing the time for preparing the subject, the cost of a BCI and the P300 classifier performance. A new algorithm to select relevant sensors is proposed, it is based on the backward elimination with a cost function related to the signal to signal-plus-noise ratio. This cost function offers better performance and avoids further mining evaluations related to the P300 recognition rate or the character recognition rate of the speller. The proposed method is tested on data recorded on 20 subjects.

[1]  Guillaume Gibert,et al.  “P300 speller” Brain-Computer Interface: Enhancement of P300 evoked potential by spatial filters , 2008, 2008 16th European Signal Processing Conference.

[2]  L. Cohen,et al.  Brain–computer interfaces: communication and restoration of movement in paralysis , 2007, The Journal of physiology.

[3]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[4]  Guillaume Gibert,et al.  xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.

[5]  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.

[6]  Gene H. Golub,et al.  Matrix computations , 1983 .

[7]  Mikhail J. Atallah,et al.  Algorithms and Theory of Computation Handbook , 2009, Chapman & Hall/CRC Applied Algorithms and Data Structures series.

[8]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[9]  Klaus-Robert Müller,et al.  Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring , 2008, Journal of Neuroscience Methods.

[10]  Hubert Cecotti,et al.  Time Delay Neural Network with Fourier transform for multiple channel detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces , 2008, 2008 16th European Signal Processing Conference.

[11]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[12]  E. W. Sellers,et al.  Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.

[13]  Max Crochemore,et al.  Algorithms and Theory of Computation Handbook , 2010 .

[14]  Mineichi Kudo,et al.  Classifier-independent feature selection on the basis of divergence criterion , 2006, Pattern Analysis and Applications.