Parametric models and spectral analysis for classification in brain-computer interfaces

Parametric modelling strategies and spectral analysis are explored in conjunction with linear discriminant analysis to facilitate an EEG based direct-brain interface for use by disabled people. A self-paced typing exercise is analysed by employing for feature extraction, respectively, an autoregressive model, an autoregressive with exogenous input model, and a time-frequency decomposition of the data. Modelling both the signal and noise is found to be more, effective than modelling the noise alone with the former yielding an accuracy of 70.7% and the latter an accuracy of 57.4%. Experiments, using the raw samples of a short-time power spectral density estimate of each trial as features, yielded an accuracy of 62.5%.

[1]  J Pardey,et al.  A review of parametric modelling techniques for EEG analysis. , 1996, Medical engineering & physics.

[2]  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.

[3]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[4]  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.

[5]  D. Childers,et al.  Event-related potentials: a critical review of methods for single-trial detection. , 1987, Critical reviews in biomedical engineering.

[6]  K. Misulis,et al.  Spehlmann's Evoked Potential Primer , 2001 .

[7]  D. Liberati,et al.  A parametric method of identification of single-trial event-related potentials in the brain , 1988, IEEE Transactions on Biomedical Engineering.

[8]  Andrzej Cichocki,et al.  Noise reduction in brain evoked potentials based on third-order correlations , 2001, IEEE Transactions on Biomedical Engineering.

[9]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[10]  E. John,et al.  Electroencephalography: Basic Principles and Applications , 2001 .

[11]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[12]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.