A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications.

Full Abstract

Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.

Author information

Author/s: Qin, Lei (L); He, Bin (B);

Affiliation: Department of Biomedical Engineering, University of Minnesota, 7-105 BSBE, 312 Church Street, Minneapolis, MN 55455, USA.

Grants: R01 EB000178-03 (Agency:NIBIB NIH HHS) ; R01EB00178 (Agency:NIBIB NIH HHS)

Journal and publication information

Publication Type: Clinical Trial; Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.

Journal: Journal of neural engineering (J Neural Eng), published in England. (Language: eng)

Reference: 2005-Dec; vol 2 (issue 4) : pp 65-72

Dates: Created 2005/11/30; Completed 2006/04/18; Revised 2008/11/20;

PMID: 16317229, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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