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- W2373330094 abstract "A key issue in the brain-computer interface(BCI) research is accurate to the EEG signal feature extraction and pattern classification,in order to get the man-machine communication and control commands.We first deal with the EEG evoked by nontarget stimuli and target stimuli by removing the meanvalue,low-pass filtering,down-sampling and so on,then extract the feature of EEG data collected by using of common space model algorithm,and then get the most excellent classification parameters by using a design based on radial basis function support vector machine classifier through the grid search method.The better classification result is obtained through dealing with the experimental data of the three subjects 10 times,its average classification accuracy rate is 99.2%.Experimental results show that this method is suitable for the brain-computer interface based on Imitating-reading." @default.
- W2373330094 created "2016-06-24" @default.
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- W2373330094 date "2012-01-01" @default.
- W2373330094 modified "2023-09-23" @default.
- W2373330094 title "Classification of Brain-Computer Interface signal based on Common Spatial Pattern and Support Vector Machine" @default.
- W2373330094 hasPublicationYear "2012" @default.
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