NM000226: eeg dataset, 4 subjects#

Zhou2016

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 4 Recordings: 24 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

Data Availability and Regeneration Instructions

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

Introduction

View full README

Data Availability and Regeneration Instructions

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

Introduction

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, https://github.com/NeuroTechX/moabb). The original study investigated a fully automated trial selection method for optimization of motor imagery based brain-computer interfaces.

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)

Dataset Information#

Dataset ID

NM000226

Title

Zhou2016

Author (year)

Zhou2016_226

Canonical

Zhou2016_NEMAR

Importable as

NM000226, Zhou2016_226, Zhou2016_NEMAR

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
Copy-paste 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},
}

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: 4

  • Recordings: 24

  • Tasks: 1

Channels & sampling rate

  • Channels: 14

  • Sampling rate (Hz): 100

  • Duration (hours): 6.271044444444445

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 528.3 MB

  • File count: 24

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: 10.82901/nemar.nm000115

Provenance

API Reference#

Use the NM000226 class to access this dataset programmatically.

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

Bases: EEGDashDataset

Zhou2016

Study:

nm000226 (NeMAR)

Author (year):

Zhou2016_226

Canonical:

Zhou2016_NEMAR

Also importable as: NM000226, Zhou2016_226, Zhou2016_NEMAR.

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#