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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mukul, M.K. Matsuno, F. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan (Mukul, M.K.) || Department of Mechanical Engineering and Science, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan (Matsuno, F.) |
| Abstract | Biomedical signals have a significant temporal structure. It is obvious to use the blind source separation(BSS) algorithm based on the time lagged covariance matrix for the independent source separation from the multichannel EEG signals. In this paper work, a novel signal preprocessing technique for the enhancement of the rhythmic information related to movement imagination has been presented. The technique is based on the BSS algorithm called as AMUSE algorithm. AMUSE algorithm estimates the separating matrix by an eigenvalue decomposition of a single time lagged covariance matrix of the EEG signals. Human brain has an asymmetrical structure. The asymmetry over the primary motor cortex areas has been incorporated for the modification of the estimated separating matrix. Preprocessing by the modified separating matrix achieve almost 100% classification accuracy between the left and right hand movement imagination. Consequently there is also a significant improvement in Cohen's kappa coefficient. |
| Starting Page | 880 |
| Ending Page | 885 |
| File Size | 239842 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781612844879 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-05-15 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Asian Control Association |
| Subject Keyword | AMUSE Algorithm Electrooculography Asymmetry Ratio Accuracy Source separation Cohen's Kappa Coefficient Classification Accuracy Training data Feature extraction Electroencephalography Covariance matrix |
| Content Type | Text |
| Resource Type | Article |
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