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.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Sun S,Zhang Cdoi
10.1007/s11517-006-0107-4subject
Has Abstractpub_date
2006-10-01 00:00:00pages
931-5issue
10eissn
0140-0118issn
1741-0444journal_volume
44pub_type
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