Bispectrum-based feature extraction technique for devising a practical brain–computer interface

The extraction of distinctly separable features from electroencephalogram (EEG) is one of the main challenges in designing a brain-computer interface (BCI). Existing feature extraction techniques for a BCI are mostly developed based on traditional signal processing techniques assuming that the signal is Gaussian and has linear characteristics. But the motor imagery (MI)-related EEG signals are highly non-Gaussian, non-stationary and have nonlinear dynamic characteristics. This paper proposes an advanced, robust but simple feature extraction technique for a MI-related BCI. The technique uses one of the higher order statistics methods, the bispectrum, and extracts the features of nonlinear interactions over several frequency components in MI-related EEG signals. Along with a linear discriminant analysis classifier, the proposed technique has been used to design an MI-based BCI. Three performance measures, classification accuracy, mutual information and Cohen's kappa have been evaluated and compared with a BCI using a contemporary power spectral density-based feature extraction technique. It is observed that the proposed technique extracts nearly recording-session-independent distinct features resulting in significantly much higher and consistent MI task detection accuracy and Cohen's kappa. It is therefore concluded that the bispectrum-based feature extraction is a promising technique for detecting different brain states.

[1]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[2]  M.R. Raghuveer,et al.  Bispectrum estimation: A digital signal processing framework , 1987, Proceedings of the IEEE.

[3]  Hagit Messer,et al.  On the principal domain of the discrete bispectrum of a stationary signal , 1995, IEEE Trans. Signal Process..

[4]  Armando Barreto,et al.  Classification of spatio-temporal EEG readiness potentials towards the development of a brain-computer interface , 1996, Proceedings of SOUTHEASTCON '96.

[5]  G. Pfurtscheller,et al.  Subject specific EEG patterns during motor imaginary [sic.: for imaginary read imagery] , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[6]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[7]  G. Pfurtscheller,et al.  Using adaptive autoregressive parameters for a brain-computer-interface experiment , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[8]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[9]  M. Erb,et al.  Activation of Cortical and Cerebellar Motor Areas during Executed and Imagined Hand Movements: An fMRI Study , 1999, Journal of Cognitive Neuroscience.

[10]  D Popivanov,et al.  Testing procedures for non-stationarity and non-linearity in physiological signals. , 1999, Mathematical biosciences.

[11]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[12]  Christa Neuper,et al.  Hidden Markov models for online classification of single trial EEG data , 2001, Pattern Recognit. Lett..

[13]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[14]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[15]  A. Schlogl,et al.  Information transfer of an EEG-based brain computer interface , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[16]  Bin He,et al.  Classification of motor imagery EEG patterns and their topographic representation , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Steven Lemm,et al.  BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.

[18]  Xiaorong Gao,et al.  Classification of single-trial electroencephalogram during finger movement , 2004, IEEE Trans. Biomed. Eng..

[19]  Fusheng Yang,et al.  BCI competition 2003-data set IV:An algorithm based on CSSD and FDA for classifying single-trial EEG , 2004, IEEE Transactions on Biomedical Engineering.

[20]  J. Sim,et al.  The kappa statistic in reliability studies: use, interpretation, and sample size requirements. , 2005, Physical therapy.

[21]  Klaus-Robert Müller,et al.  Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing , 2006, IEEE Transactions on Biomedical Engineering.

[22]  G Pfurtscheller,et al.  Cardiac responses induced during thought-based control of a virtual environment. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[23]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[24]  Clemens Brunner,et al.  Online Control of a Brain-Computer Interface Using Phase Synchronization , 2006, IEEE Transactions on Biomedical Engineering.

[25]  Zhongming Liu,et al.  An enhanced time-frequency-spatial approach for motor imagery classification , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  Ting Wu,et al.  Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition. , 2007, Medical engineering & physics.

[27]  J. Sherwood,et al.  Classification of Imagined Motor Tasks for BCI , 2008, 2008 IEEE Region 5 Conference.

[28]  Francisco Sepulveda,et al.  Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface , 2008, Inf. Sci..

[29]  Xiaorong Gao,et al.  Bipolar electrode selection for a motor imagery based brain–computer interface , 2008, Journal of neural engineering.

[30]  T.M. McGinnity,et al.  Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects , 2008, IEEE Transactions on Biomedical Engineering.

[32]  S. Gielen,et al.  The brain–computer interface cycle , 2009, Journal of neural engineering.

[33]  Damien Coyle,et al.  Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces , 2009, IEEE Computational Intelligence Magazine.

[34]  Clemens Brunner,et al.  Nonstationary Brain Source Separation for Multiclass Motor Imagery , 2010, IEEE Transactions on Biomedical Engineering.

[35]  G Pfurtscheller,et al.  Toward a hybrid brain–computer interface based on imagined movement and visual attention , 2010, Journal of neural engineering.

[36]  Clemens Brunner,et al.  Nonstationary Brain Source Separation for Multiclass Motor Imagery , 2010, IEEE Transactions on Biomedical Engineering.

[37]  Girijesh Prasad,et al.  A bispectrum approach to feature extraction for a motor imagery based brain-computer interfacing system , 2010, 2010 18th European Signal Processing Conference.

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