ArticleScientific data2025
Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The effect of rehabilitation training based on brain-computer interface on limb function in stroke patients: a systematic review and meta-analyses.Frontiers in neurologyPooled it
- From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation.Micromachines · 2026Review
- A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.Scientific data · 2026Article
- An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels.Scientific data · 2026Article
- EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in motor imagery and execution.GigaScience · 2026Article
- Closed-loop brain-computer interfaces for post-stroke sensorimotor loop restoration.Frontiers in neuroscience · 2026Review
- A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises.Scientific data · 2025Article
- Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.Scientific data · 2025Article
- Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.Scientific data · 2025Article
- Core muscle rehabilitation during early mobilization after stroke: a narrative review and clinical framework.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.