{"title":"运动想象中的人机交互：基于 STFCN 的单侧上肢康复辅助系统。","authors":"Hui Tian","doi":"10.1016/j.jneumeth.2024.110240","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component.</p></div><div><h3>New methods</h3><p>In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning.</p></div><div><h3>Comparison with existing methods</h3><p>Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets.</p></div><div><h3>Results</h3><p>To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7 %.</p></div><div><h3>Conclusion</h3><p>This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"411 ","pages":"Article 110240"},"PeriodicalIF":2.7000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance\",\"authors\":\"Hui Tian\",\"doi\":\"10.1016/j.jneumeth.2024.110240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component.</p></div><div><h3>New methods</h3><p>In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning.</p></div><div><h3>Comparison with existing methods</h3><p>Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets.</p></div><div><h3>Results</h3><p>To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7 %.</p></div><div><h3>Conclusion</h3><p>This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.</p></div>\",\"PeriodicalId\":16415,\"journal\":{\"name\":\"Journal of Neuroscience Methods\",\"volume\":\"411 \",\"pages\":\"Article 110240\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-08-05\",\"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/S0165027024001857\",\"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/S0165027024001857","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance
Background
Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component.
New methods
In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning.
Comparison with existing methods
Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets.
Results
To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7 %.
Conclusion
This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.
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.
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