Journal article 1036 views
Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
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John Q. Gan,
Francisco Sepulveda
Information Sciences, Volume: 178, Issue: 6, Pages: 1629 - 1640
Swansea University Author:
Shang-ming Zhou
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DOI (Published version): 10.1016/j.ins.2007.11.012
Abstract
This paper proposes a new feature extraction method to characterize the non-Gaussian information contained within the EEG signals using bispectrum. Moreover, the proposed method is applied to the classification of right and left motor imagery for developing EEG-based brain–computer interface systems...
| Published in: | Information Sciences |
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| ISSN: | 00200255 |
| Published: |
INFORMATION SCIENCES
2008
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa10025 |
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| Abstract: |
This paper proposes a new feature extraction method to characterize the non-Gaussian information contained within the EEG signals using bispectrum. Moreover, the proposed method is applied to the classification of right and left motor imagery for developing EEG-based brain–computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the brain-computer interface (BCI) 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate. |
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| Keywords: |
Brain–computer interfaces, Classification, Electroencephalogram (EEG), Feature extraction, Higher-order statistics, Bispectrum |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Issue: |
6 |
| Start Page: |
1629 |
| End Page: |
1640 |