NM000324: eeg dataset, 32 subjects#
Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025
Access recordings and metadata through EEGDash.
Citation: Bhoj Raj Thapa, John Boggess, Jihye Bae (2025). Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025. 10.82901/nemar.nm000324
Modality: eeg Subjects: 32 Recordings: 480 License: CC-BY-4.0 Source: nemar
Metadata: Complete (100%)
32-participant EEG dataset — Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000324
dataset = NM000324(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000324(cache_dir="./data", subject="01")
Advanced query
dataset = NM000324(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{nm000324,
title = {Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025},
author = {Bhoj Raj Thapa and John Boggess and Jihye Bae},
doi = {10.82901/nemar.nm000324},
url = {https://doi.org/10.82901/nemar.nm000324},
}
About This Dataset#
Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025.
Code: Thapa2025
Paradigm: imagery DOI: 10.1038/s41597-025-06039-9 Subjects: 23 Sessions per subject: 1-3 (loader default declares 3; deposited tree holds 49 sessions across 23 subjects, distribution: 2 subjects have 1 session (sub-02, sub-17), 16 subjects have 2 sessions, 5 subjects have 3 sessions) Events: Tgt1=1, Tgt2=2, Tgt3=3, Tgt4=4 Trial interval: [0, 4] s Runs per session: 5 File format: BrainVision (BIDS)
Thapa2025
Acquisition
Sampling rate: 250.0 Hz Number of channels: 39 Channel types: eeg=31, eog=4, misc=4 Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, FT9, FC5, FC1, FC2, FC6, FT10, T7, C3, C4, T8, TP9, CP5, CP1, CP2, CP6, TP10, P7, P3, Pz, P4, P8, O1, Oz, O2
View full README
Thapa2025
Acquisition
Sampling rate: 250.0 Hz Number of channels: 39 Channel types: eeg=31, eog=4, misc=4 Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, FT9, FC5, FC1, FC2, FC6, FT10, T7, C3, C4, T8, TP9, CP5, CP1, CP2, CP6, TP10, P7, P3, Pz, P4, P8, O1, Oz, O2 Montage: standard_1020 Hardware: Brain Products actiCHamp Plus (actiCAP snap, gel-based active electrodes) Reference: Cz Ground: GND Sensor type: scalp EEG Line frequency: 60.0 Hz Online filters: none Cap manufacturer: Brain Products Cap model: actiCAP snap Electrode type: active Auxiliary channels: EOG (4 ch, horizontal, vertical), accelerometer, audio_cue
Participants
Number of subjects: 23 Health status: healthy Age: min=18.0, max=24.0 Gender distribution: female=8, male=15 Handedness: right Species: human
Experimental Protocol
Paradigm: imagery Task type: motor execution Number of classes: 4 Class labels: Tgt1, Tgt2, Tgt3, Tgt4 Trial duration: 12.0 s Study design: Self-paced freewill reach-and-grasp: the subject freely chooses one of four cups and the timing of movement. Four cups (two water-filled, two empty) define the four classes. Feedback type: none Stimulus type: audio start/end cue Stimulus modalities: auditory Primary modality: auditory Synchronicity: self-paced Mode: offline
HED Event Annotations
Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser Tgt1
├─ Sensory-event
└─ Label/Tgt1
Tgt2
├─ Sensory-event
└─ Label/Tgt2
Tgt3
├─ Sensory-event
└─ Label/Tgt3
Tgt4
├─ Sensory-event
└─ Label/Tgt4
Paradigm-Specific Parameters
Detected paradigm: motor_imagery Number of targets: 4
Data Structure
Trials: 6808 Trials context: 6808 trials across 23 subjects and 49 sessions
Cross-Validation
Evaluation type: within_subject, cross_session
BCI Application
Applications: motor_control Environment: laboratory
Tags
Pathology: Healthy Modality: Motor Type: Research
Documentation
Description: A large EEG database of freewill reaching and grasping tasks for brain-machine interfaces: 23 subjects, 49 sessions, 6808 self-paced reach-and-grasp trials toward one of four cups. DOI: 10.1038/s41597-025-06039-9 License: CC-BY-4.0 Investigators: Bhoj Raj Thapa, John Boggess, Jihye Bae Senior author: Jihye Bae Institution: University of Kentucky Department: Department of Electrical and Computer Engineering Country: US Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.28632599 Publication year: 2025 Keywords: EEG, reach and grasp, motor execution, brain-machine interface, freewill
References
Thapa, B. R., Boggess, J., & Bae, J. (2025). A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces. Scientific Data, 12(1). https://doi.org/10.1038/s41597-025-06039-9 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb
Ethics
Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB.
Please consult the primary publication for the exact IRB/ethics committee reference.
Ethics
Conducted under the approved University of Kentucky Institutional Review Board (IRB) protocol #57031. Participants provided written consent for data collection and for de-identified data to be shared indefinitely (Thapa, Boggess & Bae 2025, Sci. Data, DOI 10.1038/s41597-025-06039-9).
Verbatim from the source:
This study was conducted following the approved University of Kentucky Institutional Review Board (IRB) protocol #57031. … Before data collection, all participants were informed of the study’s purpose and procedures, and they provided written consent for data collection and permission to have de-identified data to be shared and available indefinitely.
Source: cached paper .paper-audit/Thapa2025/paper-10_1038_s41597_025_06039_9.txt (Thapa, Boggess & Bae 2025, Sci. Data, DOI 10.1038/s41597-025-06039-9).
NEMAR Metadata#
[](https://doi.org/10.82901/nemar.nm000324) Thapa2025 ========= Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025. Dataset Overview —————-
Code: Thapa2025 Paradigm: imagery DOI: 10.1038/s41597-025-06039-9 Subjects: 23 Sessions per subject: 1-3 (loader default declares 3; deposited tree holds 49 sessions across 23 subjects, distribution: 2 subjects have 1 session (sub-02, sub-17), 16 subjects have 2 sessions, 5 subjects have 3 sessions) Events: Tgt1=1, Tgt2=2, Tgt3=3, Tgt4=4 Trial interval: [0, 4] s Runs per session: 5 File format: BrainVision (BIDS)
Acquisition#
Sampling rate: 250.0 Hz Number of channels: 39 Channel types: eeg=31, eog=4, misc=4 Channel names: Fp1, Fp2, F7, F3, Fz, F4, F8, FT9, FC5, FC1, FC2, FC6, FT10, T7, C3, C4, T8, TP9, CP5, CP1, CP2, CP6, TP10, P7, P3, Pz, P4, P8, O1, Oz, O2 Montage: standard_1020 Hardware: Brain Products actiCHamp Plus (actiCAP snap, gel-based active electrodes) Reference: Cz Ground: GND Sensor type: scalp EEG Line frequency: 60.0 Hz Online filters: none Cap manufacturer: Brain Products Cap model: actiCAP snap Electrode type: active Auxiliary channels: EOG (4 ch, horizontal, vertical), accelerometer, audio_cue
Participants#
Number of subjects: 23 Health status: healthy Age: min=18.0, max=24.0 Gender distribution: female=8, male=15 Handedness: right Species: human
Experimental Protocol#
Paradigm: imagery Task type: motor execution Number of classes: 4 Class labels: Tgt1, Tgt2, Tgt3, Tgt4 Trial duration: 12.0 s Study design: Self-paced freewill reach-and-grasp: the subject freely chooses one of four cups and the timing of movement. Four cups (two water-filled, two empty) define the four classes. Feedback type: none Stimulus type: audio start/end cue Stimulus modalities: auditory Primary modality: auditory Synchronicity: self-paced Mode: offline
HED Event Annotations#
Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser Tgt1
├─ Sensory-event └─ Label/Tgt1
- Tgt2
├─ Sensory-event └─ Label/Tgt2
- Tgt3
├─ Sensory-event └─ Label/Tgt3
- Tgt4
├─ Sensory-event └─ Label/Tgt4
Paradigm-Specific Parameters#
Detected paradigm: motor_imagery Number of targets: 4
Data Structure#
Trials: 6808 Trials context: 6808 trials across 23 subjects and 49 sessions
Cross-Validation#
Evaluation type: within_subject, cross_session
BCI Application#
Applications: motor_control Environment: laboratory
Documentation#
Description: A large EEG database of freewill reaching and grasping tasks for brain-machine interfaces: 23 subjects, 49 sessions, 6808 self-paced reach-and-grasp trials toward one of four cups. DOI: 10.1038/s41597-025-06039-9 License: CC-BY-4.0 Investigators: Bhoj Raj Thapa, John Boggess, Jihye Bae Senior author: Jihye Bae Institution: University of Kentucky Department: Department of Electrical and Computer Engineering Country: US Repository: Figshare Data URL: https://doi.org/10.6084/m9.figshare.28632599 Publication year: 2025 Keywords: EEG, reach and grasp, motor execution, brain-machine interface, freewill
References#
Thapa, B. R., Boggess, J., & Bae, J. (2025). A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces. Scientific Data, 12(1). https://doi.org/10.1038/s41597-025-06039-9 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 — Generated by MOABB 1.8.0dev0 (Mother of All BCI Benchmarks) NeuroTechX/moabb Ethics —— Ethics approval: the data analysed in this deposit were collected under the ethics approval obtained by the original investigators and reported in the primary publication cited above (see References/Documentation sections of this README). Participants gave informed consent in the source study. No new human-subject data were collected during this BIDS re-release; this NEMAR record only reformats the published source data into BIDS via MOABB. Please consult the primary publication for the exact IRB/ethics committee reference. ## Ethics Conducted under the approved University of Kentucky Institutional Review Board (IRB) protocol #57031. Participants provided written consent for data collection and for de-identified data to be shared indefinitely (Thapa, Boggess & Bae 2025, Sci. Data, DOI 10.1038/s41597-025-06039-9). Verbatim from the source: > This study was conducted following the approved University of Kentucky Institutional Review Board (IRB) protocol #57031. … Before data collection, all participants were informed of the study’s purpose and procedures, and they provided written consent for data collection and permission to have de-identified data to be shared and available indefinitely. Source: cached paper .paper-audit/Thapa2025/paper-10_1038_s41597_025_06039_9.txt (Thapa, Boggess & Bae 2025, Sci. Data, DOI 10.1038/s41597-025-06039-9).
License: CC-BY-4.0
Authors:
Bhoj Raj Thapa
John Boggess
Jihye Bae
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Channel counts: 31 ch (n=240 recordings)
Sampling frequencies (Hz)
Total recording duration: 38 h
Signal · Electrodes & live trace#
Live trace viewer — sub-1 · ses-0 · task-imagery · run-2
Showing one representative recording out of
32 subjects and 480 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 31 sensors — 31 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025 |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2025 |
Authors |
Bhoj Raj Thapa, John Boggess, Jihye Bae |
License |
CC-BY-4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000324,
title = {Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025},
author = {Bhoj Raj Thapa and John Boggess and Jihye Bae},
doi = {10.82901/nemar.nm000324},
url = {https://doi.org/10.82901/nemar.nm000324},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000324(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025
- Study:
nm000324(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000324.Modality:
eeg; Subject type:Unknown. Subjects: 32; recordings: 480; tasks: 2.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/nm000324 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000324 DOI: https://doi.org/10.82901/nemar.nm000324
Examples
>>> from eegdash.dataset import NM000324 >>> dataset = NM000324(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for nm000324 to reproduce the tutorial on this dataset.
Citation
Bhoj Raj Thapa, John Boggess, Jihye Bae (2025). Thapa2025: Freewill reach-and-grasp motor-execution dataset from Thapa et al. 2025. 10.82901/nemar.nm000324
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000324.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset