ArticleScientific data2024
An EEG motor imagery dataset for brain computer interface in acute stroke patients.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- A bibliometric analysis of electroencephalogram research in stroke: current trends and future directions.Frontiers in neurology · 2025Pooled it
- Closed-loop motor imagery brain-computer interface-assisted training for upper limb rehabilitation after subacute stroke: clinical and electroencephalographic outcomes from a randomized pilot trial.Frontiers in neurologyTrial
- A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.Scientific data · 2026Article
- A multi-paradigm and longitudinal EEG dataset including the "sixth-finger" and "affected-hand" motor imagery of stroke patients.Scientific data · 2026Article
- Noninvasive Intelligent Brain-Metasurface System for Real-Time Electromagnetic Wave Manipulation.Nanophotonics (Berlin, Germany) · 2026Article
- EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.Scientific data · 2026Article
- A lower-limb motor imagery BCI using virtual reality and novel calibration strategy in post-stroke patients.Medical & biological engineering & computing · 2026Article
- Dual-Manifold Contrastive Learning for Robust and Real-Time EEG Motor Decoding.Sensors (Basel, Switzerland) · 2026Article
- EEG-based stroke severity classification using higher-order topological features and graph convolutional networks.Frontiers in neuroscience · 2026Article
- Mental fatigue in football: methodology and experimental protocol.Frontiers in sports and active living · 2026Article
- A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises.Scientific data · 2025Article
- HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage.Scientific data · 2025Article
- A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.Scientific data · 2025Article
- A Novel Transfer Learning-Based Hybrid EEG-fNIRS Brain-Computer Interface for Intracerebral Hemorrhage Rehabilitation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- ArEEG: an Open-Access Arabic Inner Speech EEG Dataset.Scientific data · 2025Article
- Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.Scientific data · 2025Article
- GAH-TNet: A Graph Attention-Based Hierarchical Temporal Network for EEG Motor Imagery Decoding.Brain sciences · 2025Article
- Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.Journal of neuroengineering and rehabilitation · 2025Article
- EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Article
- Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.Scientific data · 2025Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
Abstract
The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients. We believe that the dataset will be very helpful for analysing brain activation and designing decoding methods that are more applicable for acute stroke patients, which will greatly facilitate research in the field of motor imagery-BCI.
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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.