NM000221: eeg dataset, 19 subjects#

Alphawaves dataset

Access recordings and metadata through EEGDash.

Citation: Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Marco Congedo (2018). Alphawaves dataset.

Modality: eeg Subjects: 19 Recordings: 19 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000221

dataset = NM000221(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000221(cache_dir="./data", subject="01")

Advanced query

dataset = NM000221(
    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{nm000221,
  title = {Alphawaves dataset},
  author = {Grégoire Cattan and Pedro Luiz Coelho Rodrigues and Marco Congedo},
}

About This Dataset#

Alphawaves dataset

Alphawaves dataset

Dataset Overview

View full README

Alphawaves dataset

Alphawaves dataset

Dataset Overview

Acquisition

  • Sampling rate: 512.0 Hz

  • Number of channels: 16

  • Channel types: eeg=16

  • Channel names: Cz, Fc5, Fc6, Fp1, Fp2, Fz, O1, O2, Oz, P3, P4, P7, P8, Pz, T7, T8

  • Montage: standard_1010

  • Hardware: g.tec g.USBamp

  • Software: OpenViBE

  • Reference: right earlobe

  • Sensor type: wet electrodes

  • Line frequency: 50.0 Hz

  • Online filters: no digital filter

Participants

  • Number of subjects: 19

  • Health status: healthy

  • Age: mean=25.8

  • Gender distribution: female=7, male=13

Experimental Protocol

  • Paradigm: rstate

  • Number of classes: 2

  • Class labels: closed, open

  • Trial duration: 10 s

  • Study design: Subjects alternated between keeping eyes closed (condition 1) and eyes open (condition 2) while EEG was recorded

HED Event Annotations

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

closed

     ├─ Experiment-structure
     └─ Rest
        └─ Close, Eye

open

├─ Experiment-structure
└─ Rest
   └─ Open, Eye

Paradigm-Specific Parameters

  • Detected paradigm: resting_state

Data Structure

  • Trials: 10

Preprocessing

  • Data state: raw

  • Preprocessing applied: False

Signal Processing

  • Feature extraction: ERS

  • Frequency bands: alpha=[8, 12] Hz

Tags

  • Pathology: Healthy

  • Modality: Resting State

  • Type: Resting-state

Documentation

  • DOI: 10.5281/zenodo.2348891

  • Associated paper DOI: hal-02086581

  • License: CC-BY-4.0

  • Investigators: Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Marco Congedo

  • Senior author: Marco Congedo

  • Contact: pedro-luiz.coelho-rodrigues@grenoble-inp.fr

  • Institution: GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP

  • Department: GIPSA-lab

  • Address: 11 rue des Mathématiques, Grenoble Campus BP46, F-38402, France

  • Country: FR

  • Repository: Zenodo

  • Data URL: https://doi.org/10.5281/zenodo.2348891

  • Publication year: 2018

  • Ethics approval: All participants provided written informed consent

  • How to acknowledge: Please cite: Cattan, Rodrigues & Congedo (2018). EEG Alpha Waves Dataset. GIPSA-lab Research Report. https://hal.science/hal-02086581

References

G. Cattan, P. L. Coelho Rodrigues, and M. Congedo, ‘EEG Alpha Waves Dataset’, 2018. Available: https://hal.archives-ouvertes.fr/hal-02086581 Rodrigues PLC. Alpha-Waves-Dataset [Internet]. Grenoble: GIPSA-lab; 2018. Available from: plcrodrigues/Alpha-Waves-Dataset Notes .. versionadded:: 1.1.0 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

Dataset Information#

Dataset ID

NM000221

Title

Alphawaves dataset

Author (year)

Cattan2017

Canonical

—

Importable as

NM000221, Cattan2017

Year

2018

Authors

Grégoire Cattan, Pedro Luiz Coelho Rodrigues, Marco Congedo

License

CC-BY-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: 19

  • Recordings: 19

  • Tasks: 1

Channels & sampling rate

  • Channels: 16

  • Sampling rate (Hz): 512.0

  • Duration (hours): 0.9618820529513888

Tags

  • Pathology: Healthy

  • Modality: Resting State

  • Type: Resting-state

Files & format

  • Size on disk: 81.7 MB

  • File count: 19

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

Electrode Layout#

Electrode layout — EEG · 16 sensors — 16 channels

Dataset Statistics#

Age distribution (n=19, range 25–25 yr)

25

Channel counts: 16 ch (n=19 recordings)

Sampling frequencies: 512.0 Hz (n=19 recordings)

Total recording duration: 57 min

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 — NM000221
HED event descriptors word cloud HED event descriptors word cloud — NM000221

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 NM000221 class to access this dataset programmatically.

class eegdash.dataset.NM000221(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Alphawaves dataset

Study:

nm000221 (NeMAR)

Author (year):

Cattan2017

Canonical:

—

Also importable as: NM000221, Cattan2017.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 19; recordings: 19; 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/nm000221 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000221

Examples

>>> from eegdash.dataset import NM000221
>>> dataset = NM000221(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.

See Also#