Available online 5 January 2023, 100751

P300 is an event-related potential (ERP) with a very low signal-to-noise ratio (SNR).

Bandpass filters were implemented to extract spectral discriminative information for detection of P300 in a visual speller application.

Significant effects were obtained using a reduced number of trials.

Proposed Filter Bank based algorithms improve the performance of the detection.

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.

Brain-computer interface

Event-related potential

P300 detection

Filter-bank

CCA

Novel methods

© 2023 AGBM. Published by Elsevier Masson SAS. All rights reserved.