Improving the Performance of P300-based Brain-Computer Interface

Bidirectional brain machine interfaces restore the lost sensory and motor function by decoding signals from the motor cortex into a control signal prompting an action and provide feedback by stimulating the sensory area. In this study, by recording EEG signals from visual cortex, extracting its P300 component by computing power spectrum density. By analysing of the frequency components, the volunteer’s intention is recognized which will be decoded as an appropriate command to move the cursor to the new position targeting the desired position indeed. We develop sensory and motor maps based on the extracted features from recorded EEG signal of the volunteers to have complete BCI system. The results of experimental findings show the effectiveness of the proposed algorithm in designing BCI systems.