EEGdash›NeMAR›NM000300

Iss. 300 · 10 subjects · 20 recordings · CC0-1.0

Dataset Brief · Pan2025

NM000300: eeg dataset, 10 subjects#

Pan2025: Cross-session motor imagery dataset from Pan et al. 2025

Access recordings and metadata through EEGDash.

Citation: Lincong Pan (2025). Pan2025: Cross-session motor imagery dataset from Pan et al. 2025. 10.82901/nemar.nm000300

Modality: eeg Subjects: 10 Recordings: 20 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — Pan2025: Cross-session motor imagery dataset from Pan et al. 2025.

Data & curation Lincong Pan
Senior author Lincong Pan
Year 2025 · Distributed via NeMAR

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 NM000300

dataset = NM000300(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 = NM000300(cache_dir="./data", subject="01")

Advanced query

dataset = NM000300(
    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{nm000300,
  title = {Pan2025: Cross-session motor imagery dataset from Pan et al. 2025},
  author = {Lincong Pan},
  doi = {10.82901/nemar.nm000300},
  url = {https://doi.org/10.82901/nemar.nm000300},
}

§ 02Study · The README

About This Dataset#

Cross-session motor imagery dataset from Pan et al. 2025.

Code: Pan2025

Paradigm: imagery DOI: 10.7910/DVN/GH74ZG Subjects: 10 Sessions per subject: 2 Events: left_hand=1, right_hand=2 Trial interval: [0, 3.996] s

DOI

Pan2025

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

Pan2025

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 Line frequency: 50.0 Hz Online filters: {‘bandpass’: [0.01, 200.0], ‘notch’: 50.0}

Participants

Number of subjects: 10 Health status: healthy Age: min=22, max=25 Gender distribution: female=3, male=7 Handedness: {‘right’: 8, ‘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=90, right_hand=90 Study design: Cued left- vs right-hand motor imagery, about 180 trials per session (4 s rest, 4 s task); trial counts vary across sessions and subjects. Session 2: first 30 trials training, remaining trials testing with online feedback. Feedback type: online feedback in session 2 test trials Stimulus type: cue Synchronicity: cue-based Mode: both

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 10 subjects performing cued left- vs right-hand motor imagery across two sessions. DOI: 10.7910/DVN/GH74ZG License: CC0-1.0 Investigators: Lincong Pan Institution: Tianjin University Country: CN Repository: Harvard Dataverse Data URL: https://doi.org/10.7910/DVN/GH74ZG Publication year: 2025

References

Pan, Lincong (2025). Cross-Session Motor Imagery EEG dataset. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/GH74ZG 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

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#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000300-blue)](https://doi.org/10.82901/nemar.nm000300) Pan2025 ======= Cross-session motor imagery dataset from Pan et al. 2025. Dataset Overview —————-

Code: Pan2025 Paradigm: imagery DOI: 10.7910/DVN/GH74ZG Subjects: 10 Sessions per subject: 2 Events: left_hand=1, right_hand=2 Trial interval: [0, 3.996] s

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 Line frequency: 50.0 Hz Online filters: {‘bandpass’: [0.01, 200.0], ‘notch’: 50.0}

Participants#

Number of subjects: 10 Health status: healthy Age: min=22, max=25 Gender distribution: female=3, male=7 Handedness: {‘right’: 8, ‘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=90, right_hand=90 Study design: Cued left- vs right-hand motor imagery, about 180 trials per session (4 s rest, 4 s task); trial counts vary across sessions and subjects. Session 2: first 30 trials training, remaining trials testing with online feedback. Feedback type: online feedback in session 2 test trials Stimulus type: cue Synchronicity: cue-based Mode: both

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 10 subjects performing cued left- vs right-hand motor imagery across two sessions. DOI: 10.7910/DVN/GH74ZG License: CC0-1.0 Investigators: Lincong Pan Institution: Tianjin University Country: CN Repository: Harvard Dataverse Data URL: https://doi.org/10.7910/DVN/GH74ZG Publication year: 2025

References#

Pan, Lincong (2025). Cross-Session Motor Imagery EEG dataset. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/GH74ZG 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 —— 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:

Lincong Pan

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000300

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 28 ch (n=20 recordings)

Sampling frequencies: 250.0 Hz (n=20 recordings)

Total recording duration: 5 h 33 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 28 ch · EEG · 250 Hz · 10 subjects, 20 recordings

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

Showing one representative recording out of 10 subjects and 20 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 · 24 sensors — 24 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.NM000300(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000300

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

class eegdash.dataset.NM000300(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Pan2025: Cross-session motor imagery dataset from Pan et al. 2025

Study:: nm000300 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000300.

Modality: eeg; Subject type: Unknown. Subjects: 10; recordings: 20; 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/nm000300 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000300 DOI: https://doi.org/10.82901/nemar.nm000300

Examples

>>> from eegdash.dataset import NM000300
>>> dataset = NM000300(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 — NM000300.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 nm000300 to reproduce the tutorial on this dataset.

Citation

Lincong Pan (2025). Pan2025: Cross-session motor imagery dataset from Pan et al. 2025. 10.82901/nemar.nm000300

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000300.

Related & sibling datasets

NM000291EEG · 14 subj NM000315EEG · 10 subj NM000294EEG · 12 subj NM000302EEG · 6 subj NM000308EEG · 2 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.nm000300

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000300`
Title	Pan2025: Cross-session motor imagery dataset from Pan et al. 2025
Author (year)	—
Canonical	—
Importable as	`NM000300`
Year	2025
Authors	Lincong Pan
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000300
Source links	OpenNeuro \| NeMAR \| Source URL

NM000300: eeg dataset, 10 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#