Variable length particle swarm optimization and multi-feature deep fusion for motor imagery EEG classification.

Brain-computer interfaces are a new pathway for communication between human body and the external environment. High classification accuracy for motor imagery electroencephalogram (EEG) signals is desirable by improving the algorithm of feature extraction and classification. A novel algorithm (VLPSO-MFDF) based on the variable length particle swarm optimization (VLPSO) and multi-feature deep fusion (MFDF) is proposed. First, each layer of the deep forest is reconstructed into two same classification modules. Then, several different features are extracted for the motor imagery EEG signal to feed separately to the classification modules. The VLPSO is used to search for the optimal weights for the probability vectors output by each classification module, which can continuously optimize the classification performance. Experimental results demonstrate that the VLPSO-MFDF algorithm can achieve higher classification accuracy for four classifications of motor imagery EEG signals compared with the traditional deep forest algorithm. The proposed method fused multi-domain features and corrected the prediction difference. It was of great significance for improving the performance of the classifier.