Particle Swarm Optimization Based Optimal Spatial-Spectral-Temporal Component Search in Motor Imagery Brain-Computer Interface

Subject dependent nature of electroencelography (EEG) signal elicits in the imagination task cause the drop of accuracy of the classifier in the brain-computer interface when the system apply in different subjects or crossing session experiment. The main components that have effect most in this problem are spatial-spectral-temporal parameter of the EEG signal that need to extract to find the optimal solution in the BCI system. In this paper we proposed a method for extracting the optimal parameters based on particle swarm optimization algorithm. First EEG signals were enhanced by Laplace and band pass filter. Optimal spatio-spectral-temporal component of Principle Component Analysis were search by Particle Swarm Optimization (PSO) using Short Time Fourier Transform features and classification error rate from Support Vector Machine (SVM) as fitness function. With optimal parameters, principle component from the STFT features were extracted and combined into single optimal feature vector. 5 fold-cross validations are applied to SVM.

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