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Content Provider | IEEE Xplore Digital Library |
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Author | Higashi, H. Tanaka, T. |
Copyright Year | 2011 |
Description | Author affiliation: Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan (Higashi, H.; Tanaka, T.) |
Abstract | The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery. |
Starting Page | 6100 |
Ending Page | 6103 |
File Size | 389240 |
Page Count | 4 |
File Format | |
ISBN | 9781424441211 |
ISSN | 1557170X |
e-ISBN | 9781457715891 |
e-ISBN | 9781424441228 |
DOI | 10.1109/IEMBS.2011.6091507 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-08-30 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Feature extraction Optimization Vectors Electroencephalography Eigenvalues and eigenfunctions Brain Finite impulse response filter |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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