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• W2123850656 abstract "In this paper we present a new scheme for brain signal processing and classification for electroencephalogram based brain-computer interfaces, by emphasizing the extraction of space-time-frequency feature as well as the combination of classifiers. In particular, we use wavelet packets as a time-frequency analysis tool and employ sparse component analysis to recover source components in the brain signals. We subsequently apply multi-class common spatial pattern filters to the signals and thus obtain important space-time-frequency features for discrimination. Furthermore, a Bayesian method is developed to boost the system, by combining multiple support vector machines in a probabilistic way. We have tested the proposed scheme on real multi-class motor imagery signals, and its efficacy has been demonstrated." @default.
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• W2123850656 date "2006-09-22" @default.
• W2123850656 modified "2023-09-28" @default.
• W2123850656 title "Signal Processing for Brain-computer Interface: Enhance Feature Extraction and Classification" @default.
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• W2123850656 doi "https://doi.org/10.1109/iscas.2006.1692910" @default.
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