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- W2348103207 abstract "Objective To discuss the feasibility, validity and advantage of using SVM classifier in cerebal function recognition, and to provide a new method of EEG signal processing and function recognition. Methods The performance of the SVM classifiers which was constructed by four kinds of kernel function was compared and discussed in detail from different aspect using the feature parameter of real measured EEG signal. Four hundreds groups of actual normal human's EEG signal in state of eyes-closed and eyes-opened were adopted in our experiment. A novel method for feature parameters transform from low dimensional to high dimensional was put forward. Results The experiment result indicated that the SVM classifier with RBF kernel was suitable to the classification and recognition of EEG signal and the highest ratio of correct recognition reached 96%. Conclusion SVM is feasible, effective and with predominant performance in EEG signal processing and brain function pattern recognition." @default.
- W2348103207 created "2016-06-24" @default.
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- W2348103207 date "2007-01-01" @default.
- W2348103207 modified "2023-09-24" @default.
- W2348103207 title "Classification and recognition of brain function based on support vector machine" @default.
- W2348103207 hasPublicationYear "2007" @default.
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