Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis
Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered muc...
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doaj-6b9361613161453d991234dddc7cfcf42021-02-13T00:01:40ZengMDPI AGSensors1424-82202021-02-01211315131510.3390/s21041315Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component AnalysisHyeon Kyu Lee0Young-Seok Choi1Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, KoreaDepartment of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, KoreaAmong various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.https://www.mdpi.com/1424-8220/21/4/1315brain-computer interface (BCI)electroencephalography (EEG)steady-state visual evoked potential (SSVEP)canonical correlation analysis (CCA)task-related component analysis (TRCA)two-step task-related component analysis (TSTRCA) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hyeon Kyu Lee Young-Seok Choi |
spellingShingle |
Hyeon Kyu Lee Young-Seok Choi Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis Sensors brain-computer interface (BCI) electroencephalography (EEG) steady-state visual evoked potential (SSVEP) canonical correlation analysis (CCA) task-related component analysis (TRCA) two-step task-related component analysis (TSTRCA) |
author_facet |
Hyeon Kyu Lee Young-Seok Choi |
author_sort |
Hyeon Kyu Lee |
title |
Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis |
title_short |
Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis |
title_full |
Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis |
title_fullStr |
Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis |
title_full_unstemmed |
Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis |
title_sort |
enhancing ssvep-based brain-computer interface with two-step task-related component analysis |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-02-01 |
description |
Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications. |
topic |
brain-computer interface (BCI) electroencephalography (EEG) steady-state visual evoked potential (SSVEP) canonical correlation analysis (CCA) task-related component analysis (TRCA) two-step task-related component analysis (TSTRCA) |
url |
https://www.mdpi.com/1424-8220/21/4/1315 |
work_keys_str_mv |
AT hyeonkyulee enhancingssvepbasedbraincomputerinterfacewithtwosteptaskrelatedcomponentanalysis AT youngseokchoi enhancingssvepbasedbraincomputerinterfacewithtwosteptaskrelatedcomponentanalysis |
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