Abstract：Aiming at nonlinear and non-stationary characteristics of motor imagery(MI) based electroencephalogram, a features extraction and patterns recognition algorithm is proposed. Firstly, overlapped filter bank(OFB) pre-processing is conducted. Then, the common spatial patterns(CSP) algorithm is applied to the filtered electroencephalogram(EEG) signals. Afterwards, the OFB-CSP features are incorporated into robust support matrix machine(RSMM) for MI patterns recognition, and the corrected particle swarm optimization(CPSO) algorithm is utilized to dynamically adjust the optimal parameters for RSMM classification. Experiments on two public datasets show that OFB pre-processing improves the discrimination of CSP features. Besides, the optimal parameters for EEG signals of individuals are searched by CSPO to the RSMM classifier. Compared with the state-of-the-arts algorithms, the proposed algorithm significantly improves MI classification accuracy. With less requirements of samples and computational resources, the proposed overlapped features strategy and parameters optimization algorithm is suitable for real-world brain computer interface application.
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2020, Vol. 33 
