Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation
Patients with stroke often suffer from motor, cognitive and speech function disorders, which seriously affect their quality of life. As an innovative technology that combines real-time assessment and rehabilitation training, electroencephalogram (EEG)-based brain-computer interface (BCI) technology...
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| Format: | Article |
| Language: | English |
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Editorial Office of Rehabilitation Medicine
2025-01-01
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| Series: | 康复学报 |
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| Online Access: | http://kfxb.publish.founderss.cn/thesisDetails?columnId=109391983&Fpath=home&index=0 |
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| author | SHU Yuhong FU Jianming YAO Yunhai ZENG Ming GU Xudong |
| author_facet | SHU Yuhong FU Jianming YAO Yunhai ZENG Ming GU Xudong |
| author_sort | SHU Yuhong |
| collection | DOAJ |
| description | Patients with stroke often suffer from motor, cognitive and speech function disorders, which seriously affect their quality of life. As an innovative technology that combines real-time assessment and rehabilitation training, electroencephalogram (EEG)-based brain-computer interface (BCI) technology has shown great potential in stroke rehabilitation. This study reviews the overview of EEG-BCI technology (definition and classification of BCI, basic characteristics of EEG signals, and types of EEG-BCI paradigms), its application in stroke rehabilitation, and its shortcomings and prospects. The EEG-BCI paradigms include motor imagery (MI), event-related potentials (ERP), steady-state evoked potentials (SSEP), and hybrid paradigms (hBCI), etc. The applications of EEG-BCI technology in stroke rehabilitation include motor function rehabilitation (upper limb movement and hand function, lower limb movement function, gait function, etc), cognitive function rehabilitation, and speech function rehabilitation. The shortcomings of the application include large signal noise, low spatial resolution, and insufficient personalized schemes. By optimizing deep learning algorithms, establishing personalized treatment systems, ethical norms for multimodal fusion, and phased clinical translation strategies, EEG-BCI technology is expected to provide more precise and safe rehabilitation plans for stroke patients. |
| format | Article |
| id | doaj-art-b04e51d2b4b946479c21c38e5dc9d278 |
| institution | DOAJ |
| issn | 2096-0328 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Rehabilitation Medicine |
| record_format | Article |
| series | 康复学报 |
| spelling | doaj-art-b04e51d2b4b946479c21c38e5dc9d2782025-08-20T03:10:30ZengEditorial Office of Rehabilitation Medicine康复学报2096-03282025-01-0119109391983Application of EEG-Based Brain-Computer Interface Technology in Stroke RehabilitationSHU YuhongFU JianmingYAO YunhaiZENG MingGU XudongPatients with stroke often suffer from motor, cognitive and speech function disorders, which seriously affect their quality of life. As an innovative technology that combines real-time assessment and rehabilitation training, electroencephalogram (EEG)-based brain-computer interface (BCI) technology has shown great potential in stroke rehabilitation. This study reviews the overview of EEG-BCI technology (definition and classification of BCI, basic characteristics of EEG signals, and types of EEG-BCI paradigms), its application in stroke rehabilitation, and its shortcomings and prospects. The EEG-BCI paradigms include motor imagery (MI), event-related potentials (ERP), steady-state evoked potentials (SSEP), and hybrid paradigms (hBCI), etc. The applications of EEG-BCI technology in stroke rehabilitation include motor function rehabilitation (upper limb movement and hand function, lower limb movement function, gait function, etc), cognitive function rehabilitation, and speech function rehabilitation. The shortcomings of the application include large signal noise, low spatial resolution, and insufficient personalized schemes. By optimizing deep learning algorithms, establishing personalized treatment systems, ethical norms for multimodal fusion, and phased clinical translation strategies, EEG-BCI technology is expected to provide more precise and safe rehabilitation plans for stroke patients.http://kfxb.publish.founderss.cn/thesisDetails?columnId=109391983&Fpath=home&index=0strokebrain-computer interfaceElectroencephalogrammotor rehabilitationcognitive rehabilitationspeech rehabilitation |
| spellingShingle | SHU Yuhong FU Jianming YAO Yunhai ZENG Ming GU Xudong Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation 康复学报 stroke brain-computer interface Electroencephalogram motor rehabilitation cognitive rehabilitation speech rehabilitation |
| title | Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation |
| title_full | Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation |
| title_fullStr | Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation |
| title_full_unstemmed | Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation |
| title_short | Application of EEG-Based Brain-Computer Interface Technology in Stroke Rehabilitation |
| title_sort | application of eeg based brain computer interface technology in stroke rehabilitation |
| topic | stroke brain-computer interface Electroencephalogram motor rehabilitation cognitive rehabilitation speech rehabilitation |
| url | http://kfxb.publish.founderss.cn/thesisDetails?columnId=109391983&Fpath=home&index=0 |
| work_keys_str_mv | AT shuyuhong applicationofeegbasedbraincomputerinterfacetechnologyinstrokerehabilitation AT fujianming applicationofeegbasedbraincomputerinterfacetechnologyinstrokerehabilitation AT yaoyunhai applicationofeegbasedbraincomputerinterfacetechnologyinstrokerehabilitation AT zengming applicationofeegbasedbraincomputerinterfacetechnologyinstrokerehabilitation AT guxudong applicationofeegbasedbraincomputerinterfacetechnologyinstrokerehabilitation |