EEGdash›NeMAR›NM000291

Iss. 291 · 14 subjects · 28 recordings · CC0-1.0

Dataset Brief · Pan2023

NM000291: eeg dataset, 14 subjects#

Pan2023: Cross-session motor imagery dataset from Pan et al. 2023

Access recordings and metadata through EEGDash.

Citation: Lincong Pan, Kun Wang, Lichao Xu, Xinwei Sun, Weibo Yi, Minpeng Xu, Dong Ming (2023). Pan2023: Cross-session motor imagery dataset from Pan et al. 2023. 10.82901/nemar.nm000291

Modality: eeg Subjects: 14 Recordings: 28 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

14-participant EEG dataset — Pan2023: Cross-session motor imagery dataset from Pan et al. 2023.

Data & curation Lincong Pan · Kun Wang · Lichao Xu · Xinwei Sun · Weibo Yi · Minpeng Xu · …
Senior author Dong Ming
Year 2023 · Distributed via NeMAR
Funding STI 2030-Major Projects 2022ZD0208900 · National Natural Science Foundation of China 62122059 · + 4 more

EEG · 28 ch250 HzBIDS 1.9.0Task · imagery2 sessions

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000291

dataset = NM000291(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = NM000291(cache_dir="./data", subject="01")

Advanced query

dataset = NM000291(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000291,
  title = {Pan2023: Cross-session motor imagery dataset from Pan et al. 2023},
  author = {Lincong Pan and Kun Wang and Lichao Xu and Xinwei Sun and Weibo Yi and Minpeng Xu and Dong Ming},
  doi = {10.82901/nemar.nm000291},
  url = {https://doi.org/10.82901/nemar.nm000291},
}

§ 02Study · The README

About This Dataset#

Cross-session motor imagery dataset from Pan et al. 2023.

Code: Pan2023

Paradigm: imagery DOI: 10.7910/DVN/251NOW Subjects: 14 Sessions per subject: 2 Events: left_hand=1, right_hand=2 Trial interval: [0, 3.996] s File format: MAT (v7.3/HDF5)

DOI

Pan2023

Acquisition

Sampling rate: 250.0 Hz Number of channels: 28 Channel types: eeg=28 Channel names: FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P5, P3, P1, Pz, P2, P4, P6

View full README

DOI

Pan2023

Acquisition

Sampling rate: 250.0 Hz Number of channels: 28 Channel types: eeg=28 Channel names: FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P5, P3, P1, Pz, P2, P4, P6 Montage: 10-10 Hardware: Neuroscan SynAmps2 Reference: nose Ground: forehead Line frequency: 50.0 Hz Online filters: {‘bandpass’: [0.01, 200.0], ‘notch’: 50.0}

Participants

Number of subjects: 14 Health status: healthy Age: min=22, max=25 Gender distribution: female=5, male=9 Handedness: {‘right’: 12, ‘left’: 2}

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.0 s Trials per class: left_hand=60, right_hand=60 Study design: Cued left- vs right-hand motor imagery on two separate days. Each session has four blocks of 30 trials (15 left, 15 right, random order) with 5 min breaks; each 7 s trial has a 1 s ‘Ready’ preparation (0.25 s beep), a 4 s task period showing a left- or right-handed rowing game animation with sound, then a 2 s rest. Released as one file per session without block boundaries. Feedback type: none Stimulus type: visual Stimulus modalities: visual, auditory Synchronicity: cue-based Mode: offline

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser left_hand

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

right_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right, Hand

Tags

Modality: Motor Type: Motor Imagery

Documentation

Description: Cross-session motor imagery EEG dataset from 14 subjects performing cued left- vs right-hand motor imagery across two sessions. DOI: 10.7910/DVN/251NOW Associated paper DOI: 10.1088/1741-2552/ad0a01 License: CC0-1.0 Investigators: Lincong Pan, Kun Wang, Lichao Xu, Xinwei Sun, Weibo Yi, Minpeng Xu, Dong Ming Institution: Tianjin University Department: School of Precision Instruments and Optoelectronics Engineering Country: CN Repository: Harvard Dataverse Data URL: https://doi.org/10.7910/DVN/251NOW Publication year: 2023 Funding: STI 2030-Major Projects 2022ZD0208900; National Natural Science Foundation of China 62122059; National Natural Science Foundation of China 62206198; National Natural Science Foundation of China 81925020; National Natural Science Foundation of China 62006014; Introduce Innovative Teams of 2021 ‘New High School 20 Items’ Project 2021GXRC071 Ethics approval: Ethics committee of Tianjin University (TJUE-2021-062) Keywords: motor imagery, cross-session, BCI, brain-computer interface, EEG, left hand, right hand

References

Pan, Lincong (2023). A cross-session motor imagery EEG dataset. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/251NOW Pan, L. et al. (2023). Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. Journal of Neural Engineering, 20(6), 066011. DOI: https://doi.org/10.1088/1741-2552/ad0a01 Notes .. versionadded:: 1.2.1 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

Approved by the Ethics Committee of Tianjin University (protocol TJUE-2021-062). The study was performed in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants (Pan et al. 2023, J. Neural Eng., DOI 10.1088/1741-2552/ad0a01).

Verbatim from the source:

This study was approved by the ethics committee of Tianjin University (TJUE-2021-062) and performed in accordance with the Declaration of Helsinki.

Source: cached paper .paper-audit/Pan2023/paper-10_1088_1741_2552_ad0a01.txt (Pan et al. 2023, J. Neural Eng., DOI 10.1088/1741-2552/ad0a01).

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000291-blue)](https://doi.org/10.82901/nemar.nm000291) Pan2023 ======= Cross-session motor imagery dataset from Pan et al. 2023. Dataset Overview —————-

Code: Pan2023 Paradigm: imagery DOI: 10.7910/DVN/251NOW Subjects: 14 Sessions per subject: 2 Events: left_hand=1, right_hand=2 Trial interval: [0, 3.996] s File format: MAT (v7.3/HDF5)

Acquisition#

Sampling rate: 250.0 Hz Number of channels: 28 Channel types: eeg=28 Channel names: FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P5, P3, P1, Pz, P2, P4, P6 Montage: 10-10 Hardware: Neuroscan SynAmps2 Reference: nose Ground: forehead Line frequency: 50.0 Hz Online filters: {‘bandpass’: [0.01, 200.0], ‘notch’: 50.0}

Participants#

Number of subjects: 14 Health status: healthy Age: min=22, max=25 Gender distribution: female=5, male=9 Handedness: {‘right’: 12, ‘left’: 2}

Experimental Protocol#

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.0 s Trials per class: left_hand=60, right_hand=60 Study design: Cued left- vs right-hand motor imagery on two separate days. Each session has four blocks of 30 trials (15 left, 15 right, random order) with 5 min breaks; each 7 s trial has a 1 s ‘Ready’ preparation (0.25 s beep), a 4 s task period showing a left- or right-handed rowing game animation with sound, then a 2 s rest. Released as one file per session without block boundaries. Feedback type: none Stimulus type: visual Stimulus modalities: visual, auditory Synchronicity: cue-based Mode: offline

HED Event Annotations#

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser left_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Left, Hand

right_hand
├─ Sensory-event, Experimental-stimulus, Visual-presentation └─ Agent-action

└─ Imagine
├─ Move └─ Right, Hand

Tags#

Modality: Motor Type: Motor Imagery

Documentation#

Description: Cross-session motor imagery EEG dataset from 14 subjects performing cued left- vs right-hand motor imagery across two sessions. DOI: 10.7910/DVN/251NOW Associated paper DOI: 10.1088/1741-2552/ad0a01 License: CC0-1.0 Investigators: Lincong Pan, Kun Wang, Lichao Xu, Xinwei Sun, Weibo Yi, Minpeng Xu, Dong Ming Institution: Tianjin University Department: School of Precision Instruments and Optoelectronics Engineering Country: CN Repository: Harvard Dataverse Data URL: https://doi.org/10.7910/DVN/251NOW Publication year: 2023 Funding: STI 2030-Major Projects 2022ZD0208900; National Natural Science Foundation of China 62122059; National Natural Science Foundation of China 62206198; National Natural Science Foundation of China 81925020; National Natural Science Foundation of China 62006014; Introduce Innovative Teams of 2021 ‘New High School 20 Items’ Project 2021GXRC071 Ethics approval: Ethics committee of Tianjin University (TJUE-2021-062) Keywords: motor imagery, cross-session, BCI, brain-computer interface, EEG, left hand, right hand

References#

Pan, Lincong (2023). A cross-session motor imagery EEG dataset. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/251NOW Pan, L. et al. (2023). Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. Journal of Neural Engineering, 20(6), 066011. DOI: https://doi.org/10.1088/1741-2552/ad0a01 Notes .. versionadded:: 1.2.1 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 Approved by the Ethics Committee of Tianjin University (protocol TJUE-2021-062). The study was performed in accordance with the Declaration of Helsinki, and written informed consent was obtained from all participants (Pan et al. 2023, J. Neural Eng., DOI 10.1088/1741-2552/ad0a01). Verbatim from the source: > This study was approved by the ethics committee of Tianjin University (TJUE-2021-062) and performed in accordance with the Declaration of Helsinki. Source: cached paper .paper-audit/Pan2023/paper-10_1088_1741_2552_ad0a01.txt (Pan et al. 2023, J. Neural Eng., DOI 10.1088/1741-2552/ad0a01).

License: CC0-1.0

Authors:

Lincong Pan
Kun Wang
Lichao Xu
Xinwei Sun
Weibo Yi
… and 2 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000291

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 28 ch (n=28 recordings)

Sampling frequencies: 250.0 Hz (n=28 recordings)

Total recording duration: 6 h 31 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 28 ch · EEG · 250 Hz · 14 subjects, 28 recordings

Live trace viewer — sub-1 · ses-0 · task-imagery · run-0

Showing one representative recording out of 14 subjects and 28 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 · 28 sensors — 28 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000291(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000291

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.NM000291(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Pan2023: Cross-session motor imagery dataset from Pan et al. 2023

Study:: nm000291 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000291.

Modality: eeg; Subject type: Unknown. Subjects: 14; recordings: 28; 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/nm000291 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000291 DOI: https://doi.org/10.82901/nemar.nm000291

Examples

>>> from eegdash.dataset import NM000291
>>> dataset = NM000291(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000291.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 nm000291 to reproduce the tutorial on this dataset.

Citation

Lincong Pan, Kun Wang, Lichao Xu, Xinwei Sun, Weibo Yi, … (2023). Pan2023: Cross-session motor imagery dataset from Pan et al. 2023. 10.82901/nemar.nm000291

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000291.

Related & sibling datasets

NM000300EEG · 10 subj NM000325EEG · 15 subj NM000294EEG · 12 subj NM000315EEG · 10 subj NM000302EEG · 6 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000291

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000291`
Title	Pan2023: Cross-session motor imagery dataset from Pan et al. 2023
Author (year)	—
Canonical	—
Importable as	`NM000291`
Year	2023
Authors	Lincong Pan, Kun Wang, Lichao Xu, Xinwei Sun, Weibo Yi, Minpeng Xu, Dong Ming
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000291
Source links	OpenNeuro \| NeMAR \| Source URL

NM000291: eeg dataset, 14 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Acquisition#

Participants#

Experimental Protocol#

HED Event Annotations#

Tags#

Documentation#

References#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#