Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection
Objective: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-stat...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923006511 |
| _version_ | 1797359978533617664 |
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| author | Ruiquan Chen Guanghua Xu Huanqing Zhang Xun Zhang Jieren Xie Peiyuan Tian Sicong Zhang Chengcheng Han |
| author_facet | Ruiquan Chen Guanghua Xu Huanqing Zhang Xun Zhang Jieren Xie Peiyuan Tian Sicong Zhang Chengcheng Han |
| author_sort | Ruiquan Chen |
| collection | DOAJ |
| description | Objective: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. Methods: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. Results: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. Conclusion: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. Significance: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing. |
| first_indexed | 2024-03-08T15:31:45Z |
| format | Article |
| id | doaj.art-8a25aa8e16d44b51bddfc41071b4cb25 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-08T15:31:45Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-8a25aa8e16d44b51bddfc41071b4cb252024-01-10T04:35:17ZengElsevierNeuroImage1095-95722024-01-01285120501Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detectionRuiquan Chen0Guanghua Xu1Huanqing Zhang2Xun Zhang3Jieren Xie4Peiyuan Tian5Sicong Zhang6Chengcheng Han7School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710054, China; The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; Corresponding author.School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, ChinaObjective: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. Methods: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. Results: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. Conclusion: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. Significance: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.http://www.sciencedirect.com/science/article/pii/S1053811923006511Brain-computer interfacesSteady-state visual evoked potentialsCanonical correlation analysisMultivariate synchronization indexFilter bank canonical correlation analysisSecond-order underdamped stochastic resonance |
| spellingShingle | Ruiquan Chen Guanghua Xu Huanqing Zhang Xun Zhang Jieren Xie Peiyuan Tian Sicong Zhang Chengcheng Han Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection NeuroImage Brain-computer interfaces Steady-state visual evoked potentials Canonical correlation analysis Multivariate synchronization index Filter bank canonical correlation analysis Second-order underdamped stochastic resonance |
| title | Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection |
| title_full | Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection |
| title_fullStr | Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection |
| title_full_unstemmed | Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection |
| title_short | Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection |
| title_sort | filter bank second order underdamped stochastic resonance analysis for implementing a short term high speed ssvep detection |
| topic | Brain-computer interfaces Steady-state visual evoked potentials Canonical correlation analysis Multivariate synchronization index Filter bank canonical correlation analysis Second-order underdamped stochastic resonance |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923006511 |
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