NM000242: eeg dataset, 22 subjects#

Name: Visual imagery EEG dataset from Gao et al 2026
Published: 2026-01-01
License: CC-BY-NC-ND-4.0

Visual imagery EEG dataset from Gao et al 2026

Access recordings and metadata through EEGDash.

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.

Modality: eeg Subjects: 22 Recordings: 125 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

Visual imagery EEG dataset from Gao et al 2026

Visual imagery EEG dataset from Gao et al 2026.

Dataset Overview

Code: Gao2026
Paradigm: imagery
DOI: 10.1038/s41597-025-06512-5

Dataset Information#

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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 22
Recordings: 125
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 1000.0
Duration (hours): 98.47829861111111

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Age distribution (n=22, range 74–74 yr)

70

Channel counts: 32 ch (n=125 recordings)

Sampling frequencies: 1000.0 Hz (n=125 recordings)

Total recording duration: 98 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000242 class to access this dataset programmatically.

class eegdash.dataset.NM000242(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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)