Improving the performance of P300-based brain–computer interface through subspace-based filtering

The detection of the presence of the P300 in the electroencephalogram (EEG) is a challenging issue in P300-based brain–computer interface (BCI). The P300-based developed in this work allows a subject to communicate 1 of 36 symbols presented on a 6×6 matrix. When a target symbol is seen by the subject, unique event-related potential (ERP) characterized by P300 is elicited. Thus, in P300 speller, accurate detection of such distinct ERP provides faster and reliable communication. In this paper, a subspace-based spatial filter was employed to enhance the detection of the presence of the P300 in the EEG. The subspace-based filter was designed by maximizing the ratio between the brain signals synchronized with the target stimulus and that with the nontarget stimulus. The processing method was evaluated offline and online on the data obtained from five subjects. The results of offline studies showed that the average accuracies of 97.5% and 90.5% were achieved in P300 detection and character recognition, respectively. The average communication rate achieved was 17.13 bits/min with an average accuracy of 89.5% using 10 electrodes during online experiments.