Statistical spatial filtering for a P300-based BCI: tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis.

The effective use of brain-computer interfaces (BCIs) in real-world environments depends on a satisfactory throughput. In a P300-based BCI, this can be attained by reducing the number of trials needed to detect the P300 signal. However, this task is hampered by the very low signal-to-noise-ratio (SNR) of P300 event related potentials. This paper proposes an efficient methodology that achieves high classification accuracy and high transfer rates for both disabled and able-bodied subjects in a standard P300-based speller system. The system was tested by three subjects with cerebral palsy (CP), two subjects with amyotrophic lateral sclerosis (ALS), and nineteen able-bodied subjects. The paper proposes the application of three statistical spatial filters. The first is a beamformer that maximizes the ratio of signal power and noise power (Max-SNR). The second is a beamformer based on the Fisher criterion (FC). The third approach cascades the FC beamformer with the Max-SNR beamformer satisfying simultaneously sub-optimally both criteria (C-FMS). The calibration process of the BCI system takes about 5 min to collect data and a couple of minutes to obtain spatial filters and classification models. Online results showed that subjects with disabilities have achieved, on average, an accuracy and transfer rate only slightly lower than able-bodied subjects. Taking 23 of the 24 participants, the averaged results achieved a transfer rate of 4.33 symbols per minute with a 91.80% accuracy, corresponding to a bandwidth of 19.18 bits per minute. This study shows the feasibility of the proposed methodology and that effective communication rates are achievable.