Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis

Baharan Kamousi; Zhongming Liu; Bin He

doi:10.1109/TNSRE.2005.847386

Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis

Baharan Kamousi, Zhongming Liu, Bin He

Biomedical Engineering

Research output: Contribution to journal › Article › peer-review

113 Scopus citations

Abstract

We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.

Original language	English (US)
Pages (from-to)	166-171
Number of pages	6
Journal	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume	13
Issue number	2
DOIs	https://doi.org/10.1109/TNSRE.2005.847386
State	Published - Jun 2005

Bibliographical note

Funding Information:
Manuscript received January 6, 2005; revised January 29, 2005; accepted February 1, 2005. This work was supported in part by the National Science Foundation (NSF) under Grant BES-0411898, in part by NSF CAREER Award BES-9875344, and in part by the National Institutes of Health under Grant R01EB00178.

Keywords

Brain-computer interface (BCI)
Dipole source analysis
Electroencephalography (EEG)
Inverse problem
Motor imagery

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Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis

Abstract

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Keywords

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