Classifying motor imagery EEG by Empirical Mode Decomposition based on spatial-time-frequency joint analysis approach

A novel spatial-time-frequency approach to classify the different mental task in brain computer interface was presented. A high resolution time-frequency spectral was achieved by using Empirical Mode Decomposition and Hilbert-Huang Transform, and the subject specific spatial-time-frequency joint features were extracted from the restricted spectral of multi-channel EEG recordings. A weighting synthetic classifier was built and used to identify the classes of the imaged motions The test results in four subjects showed that the classification accuracy varied between 77.0% and 95.0%, with an average of 85.9%, which suggested that the present method can achieve a reasonable performance in identifying imaged motions compared with previous methods.

[1]  Abbas Erfanian,et al.  A Minimax Mutual Information Scheme for Supervised Feature Extraction and Its Application to EEG-Based Brain-Computer Interfacing , 2008, EURASIP J. Adv. Signal Process..

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Slawomir J. Nasuto,et al.  A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition , 2007, Journal of Computational Neuroscience.

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

[5]  Bin He,et al.  Classifying EEG-based motor imagery tasks by means of time–frequency synthesized spatial patterns , 2004, Clinical Neurophysiology.

[6]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[7]  G Pfurtscheller,et al.  Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[9]  G. Pfurtscheller,et al.  Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.