Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients
Abstract Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04618-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849723980265553920 |
|---|---|
| author | Yuan Liu Zhuolan Gui De Yan Zhuang Wang Ruisi Gao Ningxin Han Junying Chen Jialing Wu Dong Ming |
| author_facet | Yuan Liu Zhuolan Gui De Yan Zhuang Wang Ruisi Gao Ningxin Han Junying Chen Jialing Wu Dong Ming |
| author_sort | Yuan Liu |
| collection | DOAJ |
| description | Abstract Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems. |
| format | Article |
| id | doaj-art-e00346f810584526ab722bbbc5d04c75 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-e00346f810584526ab722bbbc5d04c752025-08-20T03:10:52ZengNature PortfolioScientific Data2052-44632025-02-011211910.1038/s41597-025-04618-4Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patientsYuan Liu0Zhuolan Gui1De Yan2Zhuang Wang3Ruisi Gao4Ningxin Han5Junying Chen6Jialing Wu7Dong Ming8the Academy of Medical Engineering and Translational Medicine, Tianjin Universitythe Academy of Medical Engineering and Translational Medicine, Tianjin Universitythe Academy of Medical Engineering and Translational Medicine, Tianjin Universitythe Academy of Medical Engineering and Translational Medicine, Tianjin Universitythe Academy of Medical Engineering and Translational Medicine, Tianjin Universitythe Academy of Medical Engineering and Translational Medicine, Tianjin UniversityClinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical UniversityDepartment of Neurology, Tianjin Huanhu Hospitalthe Academy of Medical Engineering and Translational Medicine, Tianjin UniversityAbstract Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems.https://doi.org/10.1038/s41597-025-04618-4 |
| spellingShingle | Yuan Liu Zhuolan Gui De Yan Zhuang Wang Ruisi Gao Ningxin Han Junying Chen Jialing Wu Dong Ming Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients Scientific Data |
| title | Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients |
| title_full | Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients |
| title_fullStr | Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients |
| title_full_unstemmed | Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients |
| title_short | Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients |
| title_sort | lower limb motor imagery eeg dataset based on the multi paradigm and longitudinal training of stroke patients |
| url | https://doi.org/10.1038/s41597-025-04618-4 |
| work_keys_str_mv | AT yuanliu lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT zhuolangui lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT deyan lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT zhuangwang lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT ruisigao lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT ningxinhan lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT junyingchen lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT jialingwu lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients AT dongming lowerlimbmotorimageryeegdatasetbasedonthemultiparadigmandlongitudinaltrainingofstrokepatients |