Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller
IRBM, ISSN: 1959-0318, Vol: 44, Issue: 3, Page: 100751
2023
- 12Citations
- 14Captures
- 1Mentions
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Studies from Antonio Narino University in the Area of Biomedical Engineering Described (Enhancing P300 Detection Using a Band-selective Filter Bank for a Visual P300 Speller)
2023 JUN 05 (NewsRx) -- By a News Reporter-Staff News Editor at Biotech News Daily -- New research on Biotechnology - Biomedical Engineering is the
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1959031822001270; http://dx.doi.org/10.1016/j.irbm.2022.100751; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85146873822&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1959031822001270; https://dx.doi.org/10.1016/j.irbm.2022.100751
Elsevier BV
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