Motor Imagery Recognition Based on an Optimized Probabilistic Neural Network with the Particle Swarm Optimization

At present, how to efficiently and effectively identify motor imagery tasks is still a huge challenge for the development of the Brain Computer Interface (BCI) systems in the field of human rehabilitation. Therefore, this paper proposes an optimized recognition method based on temporal features and spatial features re-representation. The Local Mean Decomposition (LMD) algorithm is used to extract the Product Functions (PFs) of the Motor Imagery Electroencephalogram (MI-EEG) signals and the Common Spatial Pattern (CSP) algorithm is applied to reconstruct the spatial distribution of each PF, then the MI-EEG signals are re-represented as features with temporal and spatial characteristics. The Probabilistic Neural Network (PNN) is constructed, in which the smoothing factor is optimized by the Particle Swarm Optimization (PSO). By introducing the PSO algorithm, the PNN can be adaptively determined according to the respective conditions of different subjects or datasets. The experimental results show, compared with the Support Vector Machine (SVM) based on feature re-representation and other state-of-the-art machine learning methods, the proposed method in this paper has both high recognition accuracy and adaptability. This optimized PNN with PSO method establishes a theoretical foundation and methodological guidance for the decoding of motor imagery recognition.