EEGdash›NeMAR›NM000242
Iss. 242 · 22 subjects · 125 recordings · CC-BY-NC-ND-4.0
Dataset Brief · Visual imagery EEG dataset from Gao et al 2026

NM000242: eeg dataset, 22 subjects#

Visual imagery EEG dataset from Gao et al 2026

Citation: Jing’ao Gao, Yao Liu, Zhengshuang Li, Kaixin Huang, Fan Wang, Jiaping Xu, Lei Zhao, Tianwen Li, Yunfa Fu (2026). Visual imagery EEG dataset from Gao et al 2026.

22-participant EEG dataset — Visual imagery EEG dataset from Gao et al 2026.

Data & curation Jing'ao Gao · Yao Liu · Zhengshuang Li · Kaixin Huang · Fan Wang · Jiaping Xu · …
Year 2026 · Distributed via NeMAR
EEG · 32 ch1000 HzBIDS 1.9.0Task · imagery2 sessionsHealthyVisualOther
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000242

dataset = NM000242(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000242(cache_dir="./data", subject="01")

Advanced query

dataset = NM000242(
    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{nm000242,
  title = {Visual imagery EEG dataset from Gao et al 2026},
  author = {Jing'ao Gao and Yao Liu and Zhengshuang Li and Kaixin Huang and Fan Wang and Jiaping Xu and Lei Zhao and Tianwen Li and Yunfa Fu},
}
§ 02Study · The README

About This Dataset#

Visual imagery EEG dataset from Gao et al 2026.

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

Visual imagery EEG dataset from Gao et al 2026

dog

├─ Sensory-event
└─ Label/dog
View full README

Visual imagery EEG dataset from Gao et al 2026

dog

     ├─ Sensory-event
     └─ Label/dog

bird

     ├─ Sensory-event
     └─ Label/bird

fish

     ├─ Sensory-event
     └─ Label/fish

pentagram

     ├─ Sensory-event
     └─ Label/pentagram

square

     ├─ Sensory-event
     └─ Label/square

circle

     ├─ Sensory-event
     └─ Label/circle

scissor

     ├─ Sensory-event
     └─ Label/scissor

watch

     ├─ Sensory-event
     └─ Label/watch

cup

     ├─ Sensory-event
     └─ Label/cup

chair

├─ Sensory-event
└─ Label/chair

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: dog, bird, fish, pentagram, square, circle, scissor, watch, cup, chair

Data Structure

  • Trials: 16800

  • Trials context: 20 subjects x 2 sessions x 400 trials + 2 subjects x 1 session x 400 trials = 16800

Signal Processing

  • Classifiers: EEGNet, CSP+KNN

  • Feature extraction: CSP, deep_learning

  • Frequency bands: bandpass=[5.0, 30.0] Hz

  • Spatial filters: CSP, CAR

Cross-Validation

  • Method: train-test split

  • Evaluation type: within_subject

BCI Application

  • Applications: human_machine_interaction

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Research

Documentation

  • DOI: 10.1038/s41597-025-06512-5

  • License: CC-BY-NC-ND-4.0

  • Investigators: Jing’ao Gao, Yao Liu, Zhengshuang Li, Kaixin Huang, Fan Wang, Jiaping Xu, Lei Zhao, Tianwen Li, Yunfa Fu

  • Institution: Kunming University of Science and Technology

  • Country: CN

  • Repository: Figshare

  • Data URL: https://doi.org/10.6084/m9.figshare.30227503.v1

  • Publication year: 2026

References

Gao, J., Liu, Y., Li, Z., Huang, K., Wang, F., Xu, J., Zhao, L., Li, T., & Fu, Y. (2026). An EEG Dataset for Visual Imagery-Based Brain-Computer Interface. Scientific Data. https://doi.org/10.1038/s41597-025-06512-5 Gao, J. et al. (2026). EEG Dataset for Visual Imagery. Figshare. https://doi.org/10.6084/m9.figshare.30227503.v1 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 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=22, range 74–74 yr, mean 74.0 yr)

70
Other · 22

Channel counts: 32 ch (n=125 recordings)

Sampling frequencies: 1000.0 Hz (n=125 recordings)

Total recording duration: 98 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 1000 Hz · 22 subjects, 125 recordings
Live trace viewer — sub-13 · ses-0 · task-imagery · run-2

Showing one representative recording out of 22 subjects and 125 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 · 32 sensors — 32 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.

NEMAR pipeline statistics — NM000242
HED event descriptors word cloud HED event descriptors word cloud — NM000242
§ 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…
Full dataset metadata table

Dataset ID

NM000242

Title

Visual imagery EEG dataset from Gao et al 2026

Author (year)

Gao2026_Visual_imagery_et

Canonical

—

Importable as

NM000242, Gao2026_Visual_imagery_et

Year

2026

Authors

Jing’ao Gao, Yao Liu, Zhengshuang Li, Kaixin Huang, Fan Wang, Jiaping Xu, Lei Zhao, Tianwen Li, Yunfa Fu

License

CC-BY-NC-ND-4.0

Citation / DOI

Unknown

Source links

OpenNeuro | NeMAR | Source URL
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000242(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Gao2026_Visual_imagery_et
Canonical—
Importable asNM000242 · Gao2026_Visual_imagery_et
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000242(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Visual imagery EEG dataset from Gao et al 2026

Study:

nm000242 (NeMAR)

Author (year):

Gao2026_Visual_imagery_et

Canonical:

—

Also importable as: NM000242, Gao2026_Visual_imagery_et.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 22; recordings: 125; 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/nm000242 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000242

Examples

>>> from eegdash.dataset import NM000242
>>> dataset = NM000242(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 — NM000242.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 nm000242 to reproduce the tutorial on this dataset.

Citation

Jing'ao Gao, Yao Liu, Zhengshuang Li, Kaixin Huang, Fan Wang, … (2026). Visual imagery EEG dataset from Gao et al 2026.

Provenance

¹Contributed to nemar in BIDS format.

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

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-NC-ND-4.0 · DOI not on file
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#