{"title":"基于复杂任务相关成分分析的稳态体感诱发电位运动意象解码","authors":"","doi":"10.1016/j.cmpb.2024.108425","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and objective</h3><div>Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.</div></div><div><h3>Methods</h3><div>The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.</div></div><div><h3>Results</h3><div>The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, <em>p</em> < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (<em>p</em> < 0.001).</div></div><div><h3>Conclusions</h3><div>The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis\",\"authors\":\"\",\"doi\":\"10.1016/j.cmpb.2024.108425\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and objective</h3><div>Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.</div></div><div><h3>Methods</h3><div>The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.</div></div><div><h3>Results</h3><div>The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, <em>p</em> < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (<em>p</em> < 0.001).</div></div><div><h3>Conclusions</h3><div>The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.</div></div>\",\"PeriodicalId\":10624,\"journal\":{\"name\":\"Computer methods and programs in biomedicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer methods and programs in biomedicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169260724004188\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169260724004188","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis
Background and objective
Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.
Methods
The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.
Results
The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001).
Conclusions
The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.
期刊介绍:
To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine.
Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.