Abstract
AbstractThe 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 5min 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.
Published Version
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