Huanqing Zhang , Jun Xie , Hongwei Yu , Fangzhao Du , Zhiwei Jin , Yujie Chen
{"title":"通过随机共振增强瞬态运动开始的视觉诱发电位:单峰和跨峰噪声效应","authors":"Huanqing Zhang , Jun Xie , Hongwei Yu , Fangzhao Du , Zhiwei Jin , Yujie Chen","doi":"10.1016/j.jneumeth.2025.110589","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Motion-onset visual evoked potential (mVEP) are transient brain responses triggered by sudden motion stimuli and are widely used in brain-computer interface (BCI) systems. However, the inherently weak nature of mVEP signals poses a significant challenge to achieving reliable and accurate BCI performance. Enhancing the signal quality of mVEP responses is therefore critical for improving system robustness and usability.</div></div><div><h3>New method</h3><div>This study introduces a novel approach based on stochastic resonance (SR) theory, where appropriate levels of noise can enhance the performance of nonlinear systems such as the brain. By applying auditory and visual noise of varying intensities alongside mVEP stimuli, both unimodal SR and cross-modal SR effects were investigated. The method examines the effects of these noise conditions on brain activation and classification performance in mVEP-BCI.</div></div><div><h3>Results</h3><div>The results show that moderate levels of auditory or visual noise significantly enhance the P2 component amplitude of mVEP and improve classification accuracy in BCI tasks. In contrast, excessive noise leads to suppression of neural responses, forming an inverted U-shaped relationship between noise intensity and mVEP amplitude.</div></div><div><h3>Comparison with existing methods</h3><div>Conventional mVEP enhancement techniques typically rely on signal processing methods such as spatial filtering or feature extraction. In comparison, the proposed noise modulation strategy directly enhances neural responses, offering a biologically inspired and computationally simple alternative that complements existing approaches.</div></div><div><h3>Conclusions</h3><div>Both unimodal and cross-modal SR effectively enhance mVEP responses and BCI performance. This strategy provides new insights into SR mechanisms and supports the development of more robust mVEP-BCI systems.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110589"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing transient motion-onset visual evoked potentials via stochastic resonance: Unimodal and cross-modal noise effects\",\"authors\":\"Huanqing Zhang , Jun Xie , Hongwei Yu , Fangzhao Du , Zhiwei Jin , Yujie Chen\",\"doi\":\"10.1016/j.jneumeth.2025.110589\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Motion-onset visual evoked potential (mVEP) are transient brain responses triggered by sudden motion stimuli and are widely used in brain-computer interface (BCI) systems. However, the inherently weak nature of mVEP signals poses a significant challenge to achieving reliable and accurate BCI performance. Enhancing the signal quality of mVEP responses is therefore critical for improving system robustness and usability.</div></div><div><h3>New method</h3><div>This study introduces a novel approach based on stochastic resonance (SR) theory, where appropriate levels of noise can enhance the performance of nonlinear systems such as the brain. By applying auditory and visual noise of varying intensities alongside mVEP stimuli, both unimodal SR and cross-modal SR effects were investigated. The method examines the effects of these noise conditions on brain activation and classification performance in mVEP-BCI.</div></div><div><h3>Results</h3><div>The results show that moderate levels of auditory or visual noise significantly enhance the P2 component amplitude of mVEP and improve classification accuracy in BCI tasks. In contrast, excessive noise leads to suppression of neural responses, forming an inverted U-shaped relationship between noise intensity and mVEP amplitude.</div></div><div><h3>Comparison with existing methods</h3><div>Conventional mVEP enhancement techniques typically rely on signal processing methods such as spatial filtering or feature extraction. In comparison, the proposed noise modulation strategy directly enhances neural responses, offering a biologically inspired and computationally simple alternative that complements existing approaches.</div></div><div><h3>Conclusions</h3><div>Both unimodal and cross-modal SR effectively enhance mVEP responses and BCI performance. This strategy provides new insights into SR mechanisms and supports the development of more robust mVEP-BCI systems.</div></div>\",\"PeriodicalId\":16415,\"journal\":{\"name\":\"Journal of Neuroscience Methods\",\"volume\":\"424 \",\"pages\":\"Article 110589\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuroscience Methods\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S016502702500233X\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuroscience Methods","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016502702500233X","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Enhancing transient motion-onset visual evoked potentials via stochastic resonance: Unimodal and cross-modal noise effects
Background
Motion-onset visual evoked potential (mVEP) are transient brain responses triggered by sudden motion stimuli and are widely used in brain-computer interface (BCI) systems. However, the inherently weak nature of mVEP signals poses a significant challenge to achieving reliable and accurate BCI performance. Enhancing the signal quality of mVEP responses is therefore critical for improving system robustness and usability.
New method
This study introduces a novel approach based on stochastic resonance (SR) theory, where appropriate levels of noise can enhance the performance of nonlinear systems such as the brain. By applying auditory and visual noise of varying intensities alongside mVEP stimuli, both unimodal SR and cross-modal SR effects were investigated. The method examines the effects of these noise conditions on brain activation and classification performance in mVEP-BCI.
Results
The results show that moderate levels of auditory or visual noise significantly enhance the P2 component amplitude of mVEP and improve classification accuracy in BCI tasks. In contrast, excessive noise leads to suppression of neural responses, forming an inverted U-shaped relationship between noise intensity and mVEP amplitude.
Comparison with existing methods
Conventional mVEP enhancement techniques typically rely on signal processing methods such as spatial filtering or feature extraction. In comparison, the proposed noise modulation strategy directly enhances neural responses, offering a biologically inspired and computationally simple alternative that complements existing approaches.
Conclusions
Both unimodal and cross-modal SR effectively enhance mVEP responses and BCI performance. This strategy provides new insights into SR mechanisms and supports the development of more robust mVEP-BCI systems.
期刊介绍:
The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.