Multi-classification Motor Imagery EEG Recognition Method and Application Based on Feature Fusion
In order to achieve autonomous lower limb rehabilitation training for stroke patients,exoskeletons are gradually combined with BCI(brain computer interface).Still,multi-class MI(motor imagery)EEG(electroencephalogram)has always had characteristics of difficulty in extraction and low recognition accuracy.A multi-classification optimized support vector machine algorithm for EEG signals based on WICA(wavelet independent component analysis)and CSP(common spatial patterns)was proposed.PSO(particle swarm optimization)was used to train a support vector machine for classification.It proves that the algorithm can effectively extract EEG features and has a better recognition effect of motor imagery EEG signals.The feasibility of online and real-time EEG control was verified by combining motor imagery and exoskeleton devices.
万方数据
维普
