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-...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04618-4 |
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| Summary: | 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. |
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| ISSN: | 2052-4463 |