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

By Masaki Nakanishi, Yijun Wang, Xiaogang Chen, Yu-Te Wang, Xiaorong Gao, Tzyy-Ping Jung. IEEE Transactions on Biomedical Engineering, 2017

696 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/25e904e3bb743921a25b8998b3805ed5c052f9bf

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Citations in the corpus

Brain–Computer Interface Spellers: A Review (2018, methodology): “Recently, Nakanishi et al. reported an average ITR of 325.33 bits/min in a cue-guided task using a 40-class speller, with an accuracy of 89.83% [89].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “In the same vein, Nakanishi et al. (2018) proposed a multi-target one-phase SSVEP speller by achieving an 89.83% accuracy and 325.33 bits/min ITR.”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “visual attention on a light source flickering with a frequency above 4 Hz [30].”