EEGdash›NeMAR›NM000226

Iss. 226 · 4 subjects · 24 recordings · CC-BY-4.0

Dataset Brief · Zhou2016

NM000226: eeg dataset, 4 subjects#

Zhou2016

Citation: Bangyan Zhou, Xiaopei Wu, Zongtan Lv, Lei Zhang, Xiaojin Guo (2016). Zhou2016. 10.82901/nemar.nm000115

4-participant EEG dataset — Zhou2016.

Data & curation Bangyan Zhou · Xiaopei Wu · Zongtan Lv · Lei Zhang · Xiaojin Guo
Year 2016 · Distributed via NeMAR

EEG · 14 ch100 HzBIDS 1.9.0Task · imagery3 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 NM000226

dataset = NM000226(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 = NM000226(cache_dir="./data", subject="01")

Advanced query

dataset = NM000226(
    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{nm000226,
  title = {Zhou2016},
  author = {Bangyan Zhou and Xiaopei Wu and Zongtan Lv and Lei Zhang and Xiaojin Guo},
  doi = {10.82901/nemar.nm000115},
  url = {https://doi.org/10.82901/nemar.nm000115},
}

§ 02Study · The README

About This Dataset#

This is a derivative dataset. If any data are missing, you can use the

instructions in the code folder to download the raw data and regenerate the derivatives. README

This dataset contains EEG recordings from four subjects performing motor imagery tasks (left hand, right hand, and feet), originally published by Zhou et al. (2016). The data was reformatted into BIDS from its Zenodo version (https://zenodo.org/records/16534752), which was itself generated by MOABB (Mother of All BCI Benchmarks, NeuroTechX/moabb). The original study investigated a fully automated trial selection method for optimization of motor imagery based brain-computer interfaces.

Data Availability and Regeneration Instructions

Overview of the experiment

Four participants each completed three recording sessions separated by days to months. Each session contained two consecutive runs with inter-run breaks. Each run comprised 75 trials (25 per class: left hand, right hand, and feet imagery), for a total of 450 trials per subject across all sessions. Trials began with an auditory cue, followed by a 5-second visual arrow stimulus indicating the motor imagery task to perform, then a 4-second rest period. EEG was recorded from 14 channels placed according to the extended 10/20 system (Fp1, Fp2, FC3, FCz, FC4, C3, Cz, C4, CP3, CPz, CP4, O1, Oz, O2) at a sampling frequency of 250 Hz with a 50 Hz power line frequency.

Dataset structure

4 subjects (sub-1 through sub-4)

3 sessions per subject (ses-0, ses-1, ses-2)

View full README

Data Availability and Regeneration Instructions

Overview of the experiment

Four participants each completed three recording sessions separated by days to months. Each session contained two consecutive runs with inter-run breaks. Each run comprised 75 trials (25 per class: left hand, right hand, and feet imagery), for a total of 450 trials per subject across all sessions. Trials began with an auditory cue, followed by a 5-second visual arrow stimulus indicating the motor imagery task to perform, then a 4-second rest period. EEG was recorded from 14 channels placed according to the extended 10/20 system (Fp1, Fp2, FC3, FCz, FC4, C3, Cz, C4, CP3, CPz, CP4, O1, Oz, O2) at a sampling frequency of 250 Hz with a 50 Hz power line frequency.

Dataset structure

4 subjects (sub-1 through sub-4)

3 sessions per subject (ses-0, ses-1, ses-2)

2 runs per session (run-0, run-1)

24 EEG recordings total in EDF format

14 EEG channels, 250 Hz sampling rate

3 event types: left_hand (value=2), right_hand (value=3), feet (value=1)

Electrode positions in CapTrak coordinate system

Preprocessing

The data distributed here has undergone minimal preprocessing by MOABB prior to BIDS conversion: - Extraction of the 14 EEG channels from the original recordings - Annotation of motor imagery events (left_hand, right_hand, feet) with 5-second durations - Resampling to 250 Hz - Export to EDF format

Original and related datasets

This dataset was reformatted into BIDS from the Zenodo archive at https://zenodo.org/records/16534752. That archive was generated by MOABB v1.2.0 from the original data accompanying the publication. The original study and data are described in:

Zhou B, Wu X, Lv Z, Zhang L, Guo X (2016). A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface. PLoS ONE 11(9): e0162657. https://doi.org/10.1371/journal.pone.0162657

References

Zhou B, Wu X, Lv Z, Zhang L, Guo X (2016). A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface. PLoS ONE 11(9): e0162657. https://doi.org/10.1371/journal.pone.0162657 Appelhoff S, Sanderson M, Brooks T, et al. (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 CR, Appelhoff S, Gorgolewski KJ, et al. (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 Data curator for NEMAR version: Arnaud Delorme (UCSD, La Jolla, CA, USA)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 14 ch (n=24 recordings)

Sampling frequencies: 100.0 Hz (n=24 recordings)

Total recording duration: 6 h 16 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 14 ch · EEG · 100 Hz · 4 subjects, 24 recordings

Live trace viewer — sub-1 · ses-2 · task-imagery · run-1

Showing one representative recording out of 4 subjects and 24 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 · 14 sensors — 14 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.NM000226(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Zhou2016_226

Canonical—

Importable asNM000226 · Zhou2016_226

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

class eegdash.dataset.NM000226(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Zhou2016

Study:: nm000226 (NeMAR)
Author (year):: Zhou2016_226
Canonical:: —

Also importable as: NM000226, Zhou2016_226.

Modality: eeg. Subjects: 4; recordings: 24; 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/nm000226 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000226 DOI: https://doi.org/10.82901/nemar.nm000115

Examples

>>> from eegdash.dataset import NM000226
>>> dataset = NM000226(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 — NM000226.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 nm000226 to reproduce the tutorial on this dataset.

Citation

Bangyan Zhou, Xiaopei Wu, Zongtan Lv, Lei Zhang, Xiaojin Guo (2016). Zhou2016. 10.82901/nemar.nm000115

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000115.

Related & sibling datasets

ON003380EEG · 1 subj NM000115EEG · 4 subjSame authors NM000150EEG ON003810EEG · 10 subj ON003195EEG · 10 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000115

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000226`
Title	Zhou2016
Author (year)	`Zhou2016_226`
Canonical	—
Importable as	`NM000226`, `Zhou2016_226`
Year	2016
Authors	Bangyan Zhou, Xiaopei Wu, Zongtan Lv, Lei Zhang, Xiaojin Guo
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000115
Source links	OpenNeuro \| NeMAR \| Source URL

NM000226: eeg dataset, 4 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#