ArticleGigaScience2019
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy.
Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
108 citing papers in PubMed, 2 syntheses or guidelines pooled it, 548 citations in OpenAlex.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Use of Artificial Intelligence in the Classification of Upper-Limb Motion Using EEG and EMG Signals: A Review.Sensors (Basel, Switzerland) · 2026Pooled it
- A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface.GigaScience · 2019Trial
- A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.Scientific data · 2026Article
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- Interpretable Spectral Features for Cross-Session EEG Biometric Identification and Verification.Sensors (Basel, Switzerland) · 2026Article
- Cross-region neural signal reconstruction to lift electrode placement constraints in SSVEP brain-computer interfaces.npj biomedical innovations · 2026Article
- A novel transformer architecture for EEG decoding and neuroscientific analysis.Scientific reports · 2026Article
- SFE-GAT: Structure-Feature Evolution Graph Attention Network for Motor Imagery Decoding.Sensors (Basel, Switzerland) · 2026Article
- A Neurophysiological Stratification Framework for Intermediate Motor Imagery-BCI Users Based on Independent Event-Related Brain Dynamics.Brain sciences · 2026Article
- Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration.Nano-micro letters · 2026Review
- Visual cues in MI-BCI induce biases in EEG classification models with deep learning but not with standard machine learning.Frontiers in neuroergonomics · 2026Article
- Rotation-based metric on the Riemannian manifold of SPD matrices with applications to source data selection for brain-computer interface transfer learning.Frontiers in human neuroscience · 2026Article
- Dynamic graph based attention spectral network for motor imagery-brain computer interface.Frontiers in human neuroscience · 2026Article
- A Domain Generalization Method for EEG Based on Domain-Invariant Feature and Data Augmentation.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.Frontiers in neuroscience · 2026Article
- Cross-variability decoding for motor imagery EEG signals: a comprehensive review.Frontiers in neuroscience · 2026Review
- A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications.Scientific data · 2025Article
- A deep learning-enriched framework for analyzing brain functional connectivity.Scientific reports · 2025Article
- Enhancing classification of a large lower-limb motor imagery EEG dataset for BCI in knee pain patients.Scientific data · 2025Article
48 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature.
resultsAverage decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system.
conclusionsOur EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.
Indexed as
Identifiers
What OpenQuestion holds
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.