ArticleScientific data2025
A multi-day and high-quality EEG dataset for motor imagery brain-computer interface.
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 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- EEG vs. Hybrid EEG-fNIRS BCI for FES Control in Healthy Subjects: A Blind Randomized Study and an Open Dataset.Sensors (Basel, Switzerland) · 2026Trial
- A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.Scientific data · 2026Article
- Design and Experimental Investigation of a Multi-Level Heartbeat Sound Feedback-Based Neurofeedback System: Neural Mechanisms.Sensors (Basel, Switzerland) · 2026Article
- An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels.Scientific data · 2026Article
- An EEG Dataset for Visual Imagery-Based Brain-Computer Interface.Scientific data · 2026Article
- A confidence-gated source selection strategy for cross-session transfer in brain-computer interfaces.Frontiers in human neuroscience · 2026Article
- Cross-variability decoding for motor imagery EEG signals: a comprehensive review.Frontiers in neuroscience · 2026Review
- LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.Brain sciences · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
A key challenge in developing a robust electroencephalography (EEG)-based brain-computer interface (BCI) is obtaining reliable classification performance across multiple days. In particular, EEG-based motor imagery (MI) BCI faces large variability and low signal-to-noise ratio. To address these issues, collecting a large and reliable dataset is critical for learning of cross-session and cross-subject patterns while mitigating EEG signals inherent instability. In this study, we obtained a comprehensive MI dataset from the 2019 World Robot Conference Contest-BCI Robot Contest. We collected EEG data from 62 healthy participants across three recording sessions. This experiment includes two paradigms: (1) two-class tasks: left and right hand-grasping, (2) three-class tasks: left and right hand-grasping, and foot-hooking. The dataset comprises raw data, and preprocessed data. For the two-class data, an average classification accuracy of 85.32% was achieved using EEGNet, while the three-class data achieved an accuracy of 76.90% using deepConvNet. Different researchers can reuse the dataset according to their needs. We hope that this dataset will significantly advance MI-BCI research, particularly in addressing cross-session and cross-subject challenges.
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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.