EEG signal processing for brain-computer interfaces

This chapter is focused on recent advances in electroencephalogram (EEG) signal processing for brain computer interface (BCI) design. A general overview of BCI technologies is first presented, and then the protocol for motor imagery noninvasive BCI for mobile robot control is discussed. Our ongoing research on noninvasive BCI design based not on recorded EEG but on the brain sources that originated the EEG signal is also introduced. We propose a solution to EEG-based brain source recovering by combining two techniques, a sequential Monte Carlo method for source localization and spatial filtering by beamforming for the respective source signal estimation. The EEG inverse problem is previously studded assuming that the source localization is known. In this work for the first time the problem of inverse modeling is solved simultaneously with the problem of the respective source space localization.

[1]  Andrew W. Young,et al.  Differential effects of object-based attention on evoked potentials to fearful and disgusted faces , 2008, Neuropsychologia.

[2]  Soo-Young Lee.  Blind Source Separation and Independent Component Analysis: A Review , 2005 .

[3]  E. Basar.  EEG-brain dynamics: Relation between EEG and Brain evoked potentials , 1980 .

[4]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[5]  J. Pernier,et al.  Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human , 1996, The Journal of Neuroscience.

[6]  Hamid Reza Mohseni,et al.  A beamforming particle filter for EEG dipole source localization , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Shirley M Coyle,et al.  Brain–computer interface using a simplified functional near-infrared spectroscopy system , 2007, Journal of neural engineering.

[8]  A. Keil,et al.  Modulation of Induced Gamma Band Responses in a Perceptual Learning Task in the Human EEG , 2002, Journal of Cognitive Neuroscience.

[9]  T. Bullock,et al.  Induced Rhythms in the Brain , 1992, Brain Dynamics.

[10]  Samuel L. Braunstein,et al.  Sure Success Partial Search , 2007, Quantum Inf. Process..

[11]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[12]  Wolfgang Rosenstiel,et al.  An MEG-based brain–computer interface (BCI) , 2007, NeuroImage.

[13]  J. Capon.  High-resolution frequency-wavenumber spectrum analysis , 1969 .

[14]  Fernando Lopes da Silva,et al.  Comprar Niedermeyer's Electroencephalography, 6/e (Basic Principles, Clinical Applications, and Related Fields ) | Fernando Lopes Da Silva | 9780781789424 | Lippincott Williams & Wilkins , 2010 .

[15]  E. Dewan.  Occipital Alpha Rhythm Eye Position and Lens Accommodation , 1967, Nature.

[16]  M-X Huang,et al.  Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging , 2003, Brain Topography.

[17]  M. Hallett,et al.  An improved method for localizing electric brain dipoles , 1990, IEEE Transactions on Biomedical Engineering.

[18]  Niels Birbaumer,et al.  fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment , 2007, Comput. Intell. Neurosci..

[19]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[20]  Saeid Sanei,et al.  The application of particle filters in single trial event-related potential estimation , 2009, Physiological measurement.

[21]  G. R. Muller,et al.  Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment , 2003, Clinical Neurophysiology.

[22]  Lei Ding,et al.  Motor imagery classification by means of source analysis for brain–computer interface applications , 2004, Journal of neural engineering.

[23]  Moritz Grosse-Wentrup,et al.  Beamforming in Noninvasive Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.

[24]  J.J. Vidal,et al.  Real-time detection of brain events in EEG , 1977, Proceedings of the IEEE.

[25]  Robert Galambos,et al.  A Comparison of Certain Gamma Band (40-HZ) Brain Rhythms in Cat and Man , 1992 .

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

[27]  W. Drongelen,et al.  Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings , 2005, Clinical Neurophysiology.

[28]  Benoit M. Macq,et al.  Single-Trial EEG Source Reconstruction for Brain–Computer Interface , 2008, IEEE Transactions on Biomedical Engineering.

[29]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[30]  B. Rosen,et al.  The associations between 40 Hz-EEG and the middle latency response of the auditory evoked potential. , 1987, The International journal of neuroscience.

[31]  Cedric Nishan Canagarajah,et al.  Sequential Monte Carlo tracking by fusing multiple cues in video sequences , 2007, Image Vis. Comput..