Research on the Classification and Recognition of Multi-channel EEG Signal Based on the RBF Kernel Support Vector Machine Classification

Nonstationary randomness signal (NRS) is difficult to classify and recognize. In order to improve the performance of the classifying technique of NRS, a novel technique for classifying multi-channel EEG signal is introduced in this paper. First of all, subjects in the states of one eye open and one eye closed with a single-channel EEG feature are extracted, then the characteristics of single-channel EEG signal with bad classifying results are selected and combined into multi-channel EEG characteristics. Finally, RBF Kernel Support Vector Machine classifier is used to classify the characteristics under different states. The results show that the correct classification rate is greatly improved.