Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.
Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.
Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.
Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.Le texte complet de cet article est disponible en PDF.
P300 is an event-related potential (ERP) with a very low signal-to-noise ratio (SNR).
Bandpass filters were used to extract spectral information for P300 recognition.
Significant effects were obtained using a reduced number of trials.
Proposed Filter Bank based algorithms improve the performance of the detection.
Keywords : Brain-computer interface, Event-related potential, P300 detection, Filter-bank, CCA, Novel methods