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

Overview

Journal IEEE Trans Biomed Eng

Specialties Biomedical Engineering
Biophysics

Date 2017 Apr 25

PMID 28436836

Citations 95

Authors

Masaki Nakanishi

Yijun Wang

Xiaogang Chen

Yu-Te Wang

Xiaorong Gao

Tzyy-Ping Jung

Affiliations

Soon will be listed here.

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.

Citing Articles

Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review.

Meng J, Wei Y, Mai X, Li S, Wang X, Luo R Med Biol Eng Comput. 2025; .

PMID: 40059266 DOI: 10.1007/s11517-025-03340-y.

MP: A steady-state visual evoked potential dataset based on multiple paradigms.

Zhao X, Xu S, Geng K, Zhou T, Xu T, Wang Z iScience. 2025; 27(11):111030.

PMID: 39759080 PMC: 11700636. DOI: 10.1016/j.isci.2024.111030.

Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.

Wei Q, Li C, Wang Y, Gao X Sci Rep. 2025; 15(1):365.

PMID: 39748063 PMC: 11697369. DOI: 10.1038/s41598-024-84534-6.

Short-length SSVEP data extension by a novel generative adversarial networks based framework.

Pan Y, Li N, Zhang Y, Xu P, Yao D Cogn Neurodyn. 2024; 18(5):2925-2945.

PMID: 39555252 PMC: 11564580. DOI: 10.1007/s11571-024-10134-9.

Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.

Yin X, Yang C, Dong H, Liang J, Lin M Med Biol Eng Comput. 2024; 63(2):355-363.

PMID: 39313602 DOI: 10.1007/s11517-024-03193-x.

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