抄録
Motor imagery is the mental simulation of a motor act that includes preparation for movement, passive observations of action and mental operations of motor representations implicitly or explicitly. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through a brain machine interface. Brain machine interfaces are used to rehabilitate people suffering from neuromuscular disorders as a means of communication or control. A brain machine interface design using PSO Elman Neural Network (PSOENN-BMI) is proposed to discriminate EEG signals acquired during motor imagery for left and right hand movements. EEG is recorded at the C3 and C4 locations using noninvasive scalp electrodes placed over the motor cortex. The performance of the three state PSOENN-BMI is tested with two feature sets namely band power (BP) and principal component analysis (PCA) features. From the results it is observed that the performance of the PSOENN-BMI is better when the PCA features are used with an average efficiency range of 74.85% to 84.96%.
