NM000221: eeg dataset, 19 subjects#

Name: Alphawaves dataset
Published: 2018
License: CC-BY-4.0

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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Code: Rodrigues2017
Paradigm: rstate
DOI: https://doi.org/10.5281/zenodo.2348892

View full README

Alphawaves dataset

Alphawaves dataset

Dataset Overview

Code: Rodrigues2017
Paradigm: rstate
DOI: https://doi.org/10.5281/zenodo.2348892
Subjects: 19
Sessions per subject: 1
Events: closed=1, open=2
Trial interval: [0, 10] s

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: https://github.com/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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000221`
Title	Alphawaves dataset
Author (year)	`Cattan2017`
Canonical	`Alphawaves`, `Rodrigues2017`, `AlphaWaves`
Importable as	`NM000221`, `Cattan2017`, `Alphawaves`, `Rodrigues2017`, `AlphaWaves`
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

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:: Alphawaves, Rodrigues2017, AlphaWaves

Also importable as: NM000221, Cattan2017, Alphawaves, Rodrigues2017, AlphaWaves.

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, 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

NM000221: eeg dataset, 19 subjects#

Quickstart#

About This Dataset#

Dataset Information#

Technical Details#

API Reference#

See Also#