Impact of Spatial Filters During Sensor Selection in a Visual P300 Brain-Computer Interface

A challenge in designing a Brain-Computer Interface (BCI) is the choice of the channels, e.g. the most relevant sensors. Although a setup with many sensors can be more efficient for the detection of Event-Related Potential (ERP) like the P300, it is relevant to consider only a low number of sensors for a commercial or clinical BCI application. Indeed, a reduced number of sensors can naturally increase the user comfort by reducing the time required for the installation of the EEG (electroencephalogram) cap and can decrease the price of the device. In this study, the influence of spatial filtering during the process of sensor selection is addressed. Two of them maximize the Signal to Signal-plus-Noise Ratio (SSNR) for the different sensor subsets while the third one maximizes the differences between the averaged P300 waveform and the non P300 waveform. We show that the locations of the most relevant sensors subsets for the detection of the P300 are highly dependent on the use of spatial filtering. Applied on data from 20 healthy subjects, this study proves that subsets obtained where sensors are suppressed in relation to their individual SSNR are less efficient than when sensors are suppressed in relation to their contribution once the different selected sensors are combined for enhancing the signal. In other words, it highlights the difference between estimating the P300 projection on the scalp and evaluating the more efficient sensor subsets for a P300-BCI. Finally, this study explores the issue of channel commonality across subjects. The results support the conclusion that spatial filters during the sensor selection procedure allow selecting better sensors for a visual P300 Brain-Computer Interface.

[1]  E. John,et al.  Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.

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

[3]  E. Sellers,et al.  How many people are able to control a P300-based brain–computer interface (BCI)? , 2009, Neuroscience Letters.

[4]  Bernhard Schölkopf,et al.  Support vector channel selection in BCI , 2004, 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]  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.

[7]  Ina M. Tarkka,et al.  Source Localization of P300 from Oddball, Single Stimulus, and Omitted-Stimulus Paradigms , 2004, Brain Topography.

[8]  L. R. Quitadamo,et al.  Which Physiological Components are More Suitable for Visual ERP Based Brain–Computer Interface? A Preliminary MEG/EEG Study , 2010, Brain Topography.

[9]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[10]  Klaus-Robert Müller,et al.  On Optimal Channel Configurations for SMR-based Brain–Computer Interfaces , 2010, Brain Topography.

[11]  Jonathan R Wolpaw,et al.  Brain–computer interface systems: progress and prospects , 2007, Expert review of medical devices.

[12]  Takashi Maeno,et al.  Source modeling of the P300 event-related response using magnetoencephalography and electroencephalography measurements , 2003 .

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

[14]  Guillaume Gibert,et al.  Sensor selection for P300 speller brain computer interface , 2009, ESANN 2009.

[15]  Ali H. Shoeb,et al.  Sensor selection for energy-efficient ambulatory medical monitoring , 2009, MobiSys '09.

[16]  Alain Rakotomamonjy,et al.  BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller , 2008, IEEE Transactions on Biomedical Engineering.

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

[18]  S. Yamaguchi,et al.  Anterior and posterior association cortex contributions to the somatosensory P300 , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[19]  J. Polich.  Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[20]  Hubert Cecotti,et al.  Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  G. Pfurtscheller,et al.  How many people are able to operate an EEG-based brain-computer interface (BCI)? , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

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

[24]  Bernhard Schölkopf,et al.  Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces , 2005, EURASIP J. Adv. Signal Process..