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- W89777551 abstract "This paper presents a new method for detection of steady-sate visual evoked potential (SSVEP) in a 26-class brain-computer interface (BCI) using principal component analysis (PCA). PCA is used to decompose the multi-channel EEG signals into the components which are orthogonal. After PCA processing, the principal components (PCs) can be grouped into components related to SSVEPs and components related to brain activities. A major issue in using PCA to detect the SSVEP is the selection of proper components. In this work, we use high-order statistics to automatically identify the SSVEP components and AR power spectra to detect the frequency of SSVEP. The results of experiments on three subjects and each subject with 8 experiment sessions show that an average detection accuracy between 76.4% and 91.8% can be achieved." @default.
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- W89777551 date "2009-01-01" @default.
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- W89777551 title "Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces Using PCA and High-Order Statistics" @default.
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- W89777551 doi "https://doi.org/10.1007/978-3-642-03889-1_128" @default.
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