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

• Published in: IEEE transactions on biomedical engineering Vol. 65; no. 1; pp. 104 - 112
• Main Authors: Nakanishi, Masaki, Wang, Yijun, Chen, Xiaogang, Wang, Yu-Te, Gao, Xiaorong, Jung, Tzyy-Ping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Background noise; Biomedical monitoring; Brain; Brain - physiology; Brain-Computer Interfaces; Brain-computer interfaces (BCI); Calibration; Computer applications; Correlation analysis; EEG; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Evoked Potentials, Visual - physiology; Female; Filtering; Frequency shift keying; High speed; Human-computer interface; Humans; Implants; Information transfer; Internet; Male; Monitoring; Reproducibility; Signal Processing, Computer-Assisted; Spatial data; Spatial filtering; steady-state visual evoked potentials (SSVEP); Task Performance and Analysis; task-related component analysis (TRCA); Visual evoked potentials; Visualization; Young Adult
• DOI: 10.1109/TBME.2017.2694818
• ISSN: 0018-9294; 1558-2531

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.