Research on the Classification and Recognition of Multi-channel EEG Signal Based on the RBF Kernel Support Vector Machine Classification
Haijun Zhang · Mechanical & Electrical Engineering Technology · 2008
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.