Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis | IEEE Journals & Magazine | IEEE Xplore

Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis


Abstract:

Objective: This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward ...Show More

Abstract:

Objective: This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller. Methods: Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects. Results: The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task. Conclusion: This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI. Significance: The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 65, Issue: 1, January 2018)
Page(s): 104 - 112
Date of Publication: 19 April 2017

ISSN Information:

PubMed ID: 28436836

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