Abstract
One challenge in the current research of brain–computer interfaces (BCIs) is how to classify time-varying electroencephalographic (EEG) signals as accurately as possible. In this paper, we address this problem from the aspect of updating feature extractors and propose an adaptive feature extractor, namely adaptive common spatial patterns (ACSP). Through the weighed update of signal covariances, the most discriminative features related to the current brain states are extracted by the method of multi-class common spatial patterns (CSP). Pseudo-online simulations of EEG signal classification with a support vector machine (SVM) classifier for multi-class mental imagery tasks show the effectiveness of the proposed adaptive feature extractor.
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Acknowledgments
The authors would like to thank IDIAP Research Institute of Switzerland for providing the analyzed data. The authors are also grateful to the anonymous editor and reviewers for giving valuable comments. This work was supported by the Chinese Natural Science Foundation (60475001) and Postdoctoral Science Foundation (2005038075).
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Sun, S., Zhang, C. Adaptive feature extraction for EEG signal classification. Med Bio Eng Comput 44, 931–935 (2006). https://doi.org/10.1007/s11517-006-0107-4
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DOI: https://doi.org/10.1007/s11517-006-0107-4