Nakanishi M, Wang Y, Chen X, Wang YT, Gao X, Jung TP. Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.
IEEE Trans Biomed Eng 2018;
65:104-112. [PMID:
28436836 PMCID:
PMC5783827 DOI:
10.1109/tbme.2017.2694818]
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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.
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