Subject and Class Specific Frequency Bands Selection for Multiclass Motor Imagery Classification
- Authors
- Suk, Heung-Il; Lee, Seong-Whan
- Issue Date
- 2011
- Publisher
- WILEY
- Keywords
- brain-computer interface; frequency bands selection; motor imagery classification; ERD/ERS; electroencephalography
- Citation
- INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, v.21, no.2, pp.123 - 130
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
- Volume
- 21
- Number
- 2
- Start Page
- 123
- End Page
- 130
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/114894
- DOI
- 10.1002/ima.20283
- ISSN
- 0899-9457
- Abstract
- EEG-based discrimination among motor imagery states has been widely studied for brain-computer interfaces (BCIs) due to the great potential for real-life applications. However, in terms of designing a motor imagery-based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel method of selecting subject and class specific frequency bands based on the analysis of a channel-frequency matrix, which we call a channel-frequency map. We operate the classification process for each frequency band individually, i.e., spatial filtering, feature extraction, and classification, and determine a class label for an input EEG by considering the outputs from multiple classifiers together at the end. From our experiments on a public dataset of BCI Competition IV (2008) II-a that includes four motor imagery tasks from nine subjects, the proposed algorithm outperformed the common spatial pattern (CSP) algorithm in a broad band and a filter bank CSP algorithm on average in terms of cross-validation and session-to-session transfer rate. Furthermore, a considerable increase of classification accuracy has been achieved for certain subjects. We also would like to note that the proposed data-driven frequency bands selection method is applicable to other kinds of single-trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery-based BCI applications. (C) 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 123-130, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ima.20283
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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