NM000330: eeg dataset, 22 subjects#
Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset
Access recordings and metadata through EEGDash.
Citation: Benjakarn Uengsawapak, Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Wipamas Polpakdee, Rattanaphon Chaisaen, Poramate Manoonpong, Chanitsada Chuenchit, Gun Bhakdisongkhram, Theerawit Wilaiprasitporn (2025). Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset. 10.82901/nemar.nm000330
Modality: eeg Subjects: 22 Recordings: 44 License: CC0-1.0 Source: nemar
Metadata: Complete (100%)
22-participant EEG dataset — Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000330
dataset = NM000330(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000330(cache_dir="./data", subject="01")
Advanced query
dataset = NM000330(
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{nm000330,
title = {Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset},
author = {Benjakarn Uengsawapak and Supavit Kongwudhikunakorn and Suktipol Kiatthaveephong and Wipamas Polpakdee and Rattanaphon Chaisaen and Poramate Manoonpong and Chanitsada Chuenchit and Gun Bhakdisongkhram and Theerawit Wilaiprasitporn},
doi = {10.82901/nemar.nm000330},
url = {https://doi.org/10.82901/nemar.nm000330},
}
About This Dataset#
Sit-to-stand / stand-to-sit transition motor imagery dataset [1]_.
Code: Leelakittisin2025
Paradigm: imagery DOI: 10.5281/zenodo.20348444 Subjects: 22 Sessions per subject: 2 Events: sit_stand=21, stand_sit=32 Trial interval: [0, 4] s
Leelakittisin2025
Acquisition
Sampling rate: 1200.0 Hz Number of channels: 63 Channel types: eeg=60, eog=2, stim=1 Channel names: Fp1, Fp2, AF7, AF8, F7, F8, FT7, FT8, AF3, AF4, AFz, Fz, F1, F2, F3, F4, F5, F6, FCz, Cz, FC1, FC2, FC3, FC4, FC5, FC6, C1, C2, C3, C4, C5, C6, CPz, Pz, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, P1, P2, P3, P4, P5, P6, P7, P8, POz, Oz, PO3, PO4, PO7, PO8, PO9, PO10, O1, O2
View full README
Leelakittisin2025
Acquisition
Sampling rate: 1200.0 Hz Number of channels: 63 Channel types: eeg=60, eog=2, stim=1 Channel names: Fp1, Fp2, AF7, AF8, F7, F8, FT7, FT8, AF3, AF4, AFz, Fz, F1, F2, F3, F4, F5, F6, FCz, Cz, FC1, FC2, FC3, FC4, FC5, FC6, C1, C2, C3, C4, C5, C6, CPz, Pz, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, P1, P2, P3, P4, P5, P6, P7, P8, POz, Oz, PO3, PO4, PO7, PO8, PO9, PO10, O1, O2 Montage: 10-05 Line frequency: 50.0 Hz Auxiliary channels: EOG (2 ch, horizontal, vertical), EMG (6 ch)
Participants
Number of subjects: 22 Health status: healthy Age: min=18.0, max=30.0 Gender distribution: male=15, female=7 Handedness: {‘right’: 19, ‘left’: 2, ‘both’: 1} BCI experience: mixed (6 of 22 with prior EEG experience)
Experimental Protocol
Paradigm: imagery Number of classes: 2 Class labels: sit_stand, stand_sit Trial duration: 4.0 s Trials per class: sit_stand=20, stand_sit=20 Study design: Sit-to-stand and stand-to-sit transitions performed under both motor execution and motor imagery conditions; trigger codes at channel 63 mark each transition/rest event. MI: two rounds of 10 trials per task per session (readme index table). Mode: offline
HED Event Annotations
Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser sit_stand
├─ Sensory-event
└─ Label/sit_stand
stand_sit
├─ Sensory-event
└─ Label/stand_sit
Tags
Modality: Motor Type: Motor Imagery
Documentation
Description: First public EEG dataset explicitly targeting sit-to-stand and stand-to-sit transitions during motor execution and motor imagery, from 22 healthy participants with 60-channel EEG, EOG and EMG. DOI: 10.5281/zenodo.20348444 License: CC0-1.0 Investigators: Benjakarn Uengsawapak, Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Wipamas Polpakdee, Rattanaphon Chaisaen, Poramate Manoonpong, Chanitsada Chuenchit, Gun Bhakdisongkhram, Theerawit Wilaiprasitporn Institution: Vidyasirimedhi Institute of Science and Technology Country: TH Repository: Zenodo Publication year: 2025
References
Leelakittisin, B. (readme byline: Uengsawapak, B.), Kongwudhikunakorn, S., Kiatthaveephong, S., Polpakdee, W., Chaisaen, R., Manoonpong, P., Chuenchit, C., Bhakdisongkhram, G., & Wilaiprasitporn, T. (2025). EEG-Based Dataset Explicitly Targeting the Transitions between Sitting and Standing for Exploring Neural Activation Patterns in Motor Imagery and Execution. Zenodo. DOI: https://doi.org/10.5281/zenodo.20348444 .. versionadded:: 1.8 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.
NEMAR Metadata#
[](https://doi.org/10.82901/nemar.nm000330) Leelakittisin2025 ================= Sit-to-stand / stand-to-sit transition motor imagery dataset [1]_. Dataset Overview —————-
Code: Leelakittisin2025 Paradigm: imagery DOI: 10.5281/zenodo.20348444 Subjects: 22 Sessions per subject: 2 Events: sit_stand=21, stand_sit=32 Trial interval: [0, 4] s
Acquisition#
Sampling rate: 1200.0 Hz Number of channels: 63 Channel types: eeg=60, eog=2, stim=1 Channel names: Fp1, Fp2, AF7, AF8, F7, F8, FT7, FT8, AF3, AF4, AFz, Fz, F1, F2, F3, F4, F5, F6, FCz, Cz, FC1, FC2, FC3, FC4, FC5, FC6, C1, C2, C3, C4, C5, C6, CPz, Pz, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, P1, P2, P3, P4, P5, P6, P7, P8, POz, Oz, PO3, PO4, PO7, PO8, PO9, PO10, O1, O2 Montage: 10-05 Line frequency: 50.0 Hz Auxiliary channels: EOG (2 ch, horizontal, vertical), EMG (6 ch)
Participants#
Number of subjects: 22 Health status: healthy Age: min=18.0, max=30.0 Gender distribution: male=15, female=7 Handedness: {‘right’: 19, ‘left’: 2, ‘both’: 1} BCI experience: mixed (6 of 22 with prior EEG experience)
Experimental Protocol#
Paradigm: imagery Number of classes: 2 Class labels: sit_stand, stand_sit Trial duration: 4.0 s Trials per class: sit_stand=20, stand_sit=20 Study design: Sit-to-stand and stand-to-sit transitions performed under both motor execution and motor imagery conditions; trigger codes at channel 63 mark each transition/rest event. MI: two rounds of 10 trials per task per session (readme index table). Mode: offline
HED Event Annotations#
Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser sit_stand
├─ Sensory-event └─ Label/sit_stand
- stand_sit
├─ Sensory-event └─ Label/stand_sit
Documentation#
Description: First public EEG dataset explicitly targeting sit-to-stand and stand-to-sit transitions during motor execution and motor imagery, from 22 healthy participants with 60-channel EEG, EOG and EMG. DOI: 10.5281/zenodo.20348444 License: CC0-1.0 Investigators: Benjakarn Uengsawapak, Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Wipamas Polpakdee, Rattanaphon Chaisaen, Poramate Manoonpong, Chanitsada Chuenchit, Gun Bhakdisongkhram, Theerawit Wilaiprasitporn Institution: Vidyasirimedhi Institute of Science and Technology Country: TH Repository: Zenodo Publication year: 2025
References#
Leelakittisin, B. (readme byline: Uengsawapak, B.), Kongwudhikunakorn, S., Kiatthaveephong, S., Polpakdee, W., Chaisaen, R., Manoonpong, P., Chuenchit, C., Bhakdisongkhram, G., & Wilaiprasitporn, T. (2025). EEG-Based Dataset Explicitly Targeting the Transitions between Sitting and Standing for Exploring Neural Activation Patterns in Motor Imagery and Execution. Zenodo. DOI: https://doi.org/10.5281/zenodo.20348444 .. versionadded:: 1.8 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.
License: CC0-1.0
Authors:
Benjakarn Uengsawapak
Supavit Kongwudhikunakorn
Suktipol Kiatthaveephong
Wipamas Polpakdee
Rattanaphon Chaisaen
… and 4 more
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Channel counts: 60 ch (n=44 recordings)
Sampling frequencies: 1200.0 Hz (n=44 recordings)
Total recording duration: 30 h
Signal · Electrodes & live trace#
Live trace viewer — sub-11 · ses-1 · task-imagery · run-0
Showing one representative recording out of
22 subjects and 44 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 · 60 sensors — 60 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 |
Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2025 |
Authors |
Benjakarn Uengsawapak, Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Wipamas Polpakdee, Rattanaphon Chaisaen, Poramate Manoonpong, Chanitsada Chuenchit, Gun Bhakdisongkhram, Theerawit Wilaiprasitporn |
License |
CC0-1.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000330,
title = {Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset},
author = {Benjakarn Uengsawapak and Supavit Kongwudhikunakorn and Suktipol Kiatthaveephong and Wipamas Polpakdee and Rattanaphon Chaisaen and Poramate Manoonpong and Chanitsada Chuenchit and Gun Bhakdisongkhram and Theerawit Wilaiprasitporn},
doi = {10.82901/nemar.nm000330},
url = {https://doi.org/10.82901/nemar.nm000330},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000330(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset
- Study:
nm000330(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000330.Modality:
eeg; Subject type:Unknown. Subjects: 22; recordings: 44; tasks: 1.- 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/nm000330 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000330 DOI: https://doi.org/10.82901/nemar.nm000330
Examples
>>> from eegdash.dataset import NM000330 >>> dataset = NM000330(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 nm000330 to reproduce the tutorial on this dataset.
Citation
Benjakarn Uengsawapak, Supavit Kongwudhikunakorn, Suktipol Kiatthaveephong, Wipamas Polpakdee, Rattanaphon Chaisaen, … (2025). Leelakittisin2025: Sit-to-stand / stand-to-sit transition motor imagery dataset. 10.82901/nemar.nm000330
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000330.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset