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Cited by 17EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesGaze Tracking and Assistive Technology

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Increasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification

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Shuqfa, Zaid, Lakas, Abderrahmane, Belkacem, Abdelkader Nasreddine·2024·Data in Brief
DOI 10.1016/j.dib.2024.110181PMC PMC10998040
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Abstract

A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execution of movements. Although the PhysioNet EEG Motor Movement/Imagery Dataset is currently the largest EEG dataset in the literature, relatively few studies have used it to decode MI trials. In the present study, we curated and cleaned this dataset to store it in an accessible format that is convenient for quick exploitation, decoding, and classification using recent integrated development environments. We dropped six subjects owing to anomalies in EEG recordings and pre-possessed the rest, resulting in 103 subjects spanning four MI and four motor execution tasks. The annotations were coded to correspond to different tasks using numerical values. The resulting dataset is stored in both MATLAB structure and CSV files to ensure ease of access and organization. We believe that improving the accessibility of this dataset will help EEG-based MI-BCI decoding and classification, enabling more reliable real-life applications. The convenience and ease of access of this dataset may therefore lead to improvements in cross-subject classification and transfer learning.

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Crossref · 10.1016/j.dib.2024.110181

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Fig. 1 Dataset content. (a) EEGMMIDB MATLAB structure and (b) eegmmidb folder containing csv files.
Fig. 1 Dataset content. (a) EEGMMIDB MATLAB structure and (b) eegmmidb folder containing csv files.CC BY · PubMed Central
Fig. 2 EEG recordings from Subject 1. First 17 s of run file number 4 (imagine opening and closing right vs. left fists). Band-Pass filtered at 8–30 Hz, where the x- and y-axes represent time and the 64 electrodes, respectively.
Fig. 2 EEG recordings from Subject 1. First 17 s of run file number 4 (imagine opening and closing right vs. left fists). Band-Pass filtered at 8–30 Hz, where the x- and y-axes represent time and the 64 electrodes, respectively.CC BY · PubMed Central
Fig. 3 Count of trials in each run. Each point represents the number of trials in a run file. The blue curve shows the number of T0 trials while the orange and yellow curves represent T1 and T2, respectively. In most runs, T0 maintained an expected 15 occurrences, while T1 and T2 appeared either 7 or 8 times interchangeably. Some anomalies appeared when moving toward later subjects where the pattern was insufficiently clear.
Fig. 3 Count of trials in each run. Each point represents the number of trials in a run file. The blue curve shows the number of T0 trials while the orange and yellow curves represent T1 and T2, respectively. In most runs, T0 maintained an expected 15 occurrences, while T1 and T2 appeared either 7 or 8 times interchangeably. Some anomalies appeared when moving toward later subjects where the pattern was insufficiently clear.CC BY · PubMed Central
Fig. 4 Transforming Annotations arrays.
Fig. 4 Transforming Annotations arrays.CC BY · PubMed Central
Fig. 5 Lengths of runs and trials: Total length of each run in seconds in comparison to the total length of annotations in each run. The x-axis represents the runs of the subjects. The tick marks show subjects (from Run 3 to Run 14) where every data point on the chart represents one run.
Fig. 5 Lengths of runs and trials: Total length of each run in seconds in comparison to the total length of annotations in each run. The x-axis represents the runs of the subjects. The tick marks show subjects (from Run 3 to Run 14) where every data point on the chart represents one run.CC BY · PubMed Central

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Metadata

Authors
Shuqfa, Z., Lakas, A., & Belkacem, A. N.
Affiliations
United Arab Emirates University (AE)
Corresponding author
  • Shuqfa, Zaid
  • Lakas, Abderrahmane
ORCID
Venue
Data in Brief
Publisher
Elsevier BV
Published
2024-06
Volume
54
Pages
110181
ISSN
2352-3409 (ISSN-L)
DOI
10.1016/j.dib.2024.110181
PMC
PMC10998040
OpenAlex
W4391755881
Cited by
16
References
16
Citations by year
2026: 5 · 2025: 10 · 2024: 1
Type
data paper
Language
en
Open access
Licence
Locations
  • Data in Brief (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Rabdan Academy Digital Repository (Rabdan Academy) (submittedVersion, cc-by)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords
brain-computer interface, motor imagery, EEG, data curation, EEG preprocessing, motor execution, cross-subject classification, transfer learning, data annotation

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CoChat. (2026). Citation check: Increasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification. https://app.cochat.ai/papers/10.1016/j.dib.2024.110181
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Shuqfa, Z., Lakas, A., & Belkacem, A. N. (2024). Increasing accessibility to a large brain–computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification. Data in Brief. https://doi.org/10.1016/j.dib.2024.110181