Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy.

Full Abstract

The goal of the present study is to employ the source imaging methods such as cortical current density estimation for the classification of left- and right-hand motor imagery tasks, which may be used for brain-computer interface (BCI) applications. The scalp recorded EEG was first preprocessed by surface Laplacian filtering, time-frequency filtering, noise normalization and independent component analysis. Then the cortical imaging technique was used to solve the EEG inverse problem. Cortical current density distributions of left and right trials were classified from each other by exploiting the concept of Von Neumann entropy. The proposed method was tested on three human subjects (180 trials each) and a maximum accuracy of 91.5% and an average accuracy of 88% were obtained. The present results confirm the hypothesis that source analysis methods may improve accuracy for classification of motor imagery tasks. The present promising results using source analysis for classification of motor imagery enhances our ability of performing source analysis from single trial EEG data recorded on the scalp, and may have applications to improved BCI systems.

Author information

Author/s: Kamousi, Baharan (B); Amini, Ali Nasiri (AN); He, Bin (B);

Affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA.

Grants: R01 EB00178 (Agency:NIBIB NIH HHS)

Journal and publication information

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

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

Reference: 2007-Jun; vol 4 (issue 2) : pp 17-25

Dates: Created 2007/04/05; Completed 2007/09/25; Revised 2007/12/03;

PMID: 17409476, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: 18 Feb 2009 00:00:00)

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