Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 29, Issue -, Pages 1998-2007Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2021.3114340
Keywords
Task analysis; Electroencephalography; Training; Redundancy; Benchmark testing; Visualization; Steady-state; Brain-computer interface (BCI); steady-state visual evoked potential (SSVEP); electroencephalography (EEG); task-related component analysis (TRCA); task-discriminant component analysis (TDCA)
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Funding
- Key-Area Research and Development Program of Guangdong Province [2018B030339001]
- Doctoral Brain+X Seed Grant Program of Tsinghua University
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB32040200]
- Beijing Science and Technology Program [Z201100004420015]
- National Natural Science Foundation of China [62171473, 61431007]
- National Key Research and Development Program of China [2017YFB1002505]
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The study introduces a novel method called task-discriminant component analysis (TDCA) to enhance the performance of individually calibrated SSVEP-BCI. Results from experiments and data analysis show that TDCA outperforms other competing methods in terms of performance.
A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.
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