期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 65, 期 1, 页码 104-112出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2017.2694818
关键词
Brain-computer interfaces (BCI); electroencephalography (EEG); steady-state visual evoked potentials (SSVEP); task-related component analysis (TRCA)
资金
- Army Research Laboratory [W911NF-10-2-0022]
- National Institute of Healths [1R21EY025056-01]
- Recruitment Program for Young Professionals
- National Natural Science Foundation of China [61671424, 61603416]
- Young Talents Lift Project of Chinese Association of Science and Technology
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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