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Novel frequency-based approach for detection of steady-state visual evoked potentials for realization of practical brain computer interfaces

Novel frequency-based approach for detection of steady-state visual evoked potentials for... Various algorithms for recognizing Steady-State Visual Evoked Potentials have been proposed over the last decade for realizing Brain-Computer Interfaces. However, frequency-domain techniques aside from classical FFT have been generally neglected. While close to perfect accuracies have been reported in SSVEP-based BCI studies, achieving high accuracy in a realistic scenario is still challenging. Here several frequency-domain algorithms were evaluated for SSVEP detection for the first time, and a new algorithm based on spectral averaging on resampled signal (SAoRS) was proposed, when a single EEG channel and high-frequency flickers were considered to improve user experience. Spectral Envelope (SE) and Maximum Entropy (ME) methods outperformed Burg, MUSIC, and Welch for processing window lengths of 0.5–2 s. The newly developed SAoRS algorithm significantly outperformed SE and the benchmark CCA algorithms for 0.5 s processing window. The results suggest that Spectral Envelop and SAoRS algorithms can provide high accuracies in SSVEP BCI systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Novel frequency-based approach for detection of steady-state visual evoked potentials for realization of practical brain computer interfaces

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Novel frequency-based approach for detection of steady-state visual evoked potentials for realization of practical brain computer interfaces

Abstract

Various algorithms for recognizing Steady-State Visual Evoked Potentials have been proposed over the last decade for realizing Brain-Computer Interfaces. However, frequency-domain techniques aside from classical FFT have been generally neglected. While close to perfect accuracies have been reported in SSVEP-based BCI studies, achieving high accuracy in a realistic scenario is still challenging. Here several frequency-domain algorithms were evaluated for SSVEP detection for the first time,...
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Publisher
Taylor & Francis
Copyright
© 2022 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2022.2050513
Publisher site
See Article on Publisher Site

Abstract

Various algorithms for recognizing Steady-State Visual Evoked Potentials have been proposed over the last decade for realizing Brain-Computer Interfaces. However, frequency-domain techniques aside from classical FFT have been generally neglected. While close to perfect accuracies have been reported in SSVEP-based BCI studies, achieving high accuracy in a realistic scenario is still challenging. Here several frequency-domain algorithms were evaluated for SSVEP detection for the first time, and a new algorithm based on spectral averaging on resampled signal (SAoRS) was proposed, when a single EEG channel and high-frequency flickers were considered to improve user experience. Spectral Envelope (SE) and Maximum Entropy (ME) methods outperformed Burg, MUSIC, and Welch for processing window lengths of 0.5–2 s. The newly developed SAoRS algorithm significantly outperformed SE and the benchmark CCA algorithms for 0.5 s processing window. The results suggest that Spectral Envelop and SAoRS algorithms can provide high accuracies in SSVEP BCI systems.

Journal

Brain-Computer Interfaces – Taylor & Francis

Published: Jul 3, 2022

Keywords: Brain computer interface (BCI); steady-state visual evoked potential (SSVEP); feature extraction; frequency-domain; electroencephalogram (EEG)

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