EEGdash›NeMAR›NM000341

Iss. 341 · 12 subjects · 12 recordings · CC-BY-4.0

Dataset Brief · Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Displ…

NM000341: eeg dataset, 12 subjects#

Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD)

Citation: G. Cattan, P. L. C. Rodrigues, M. Congedo (2019). Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD). 10.5281/zenodo.2617084

12-participant EEG dataset — Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD).

Data & curation G. Cattan · P. L. C. Rodrigues · M. Congedo
Year 2019 · Distributed via NeMAR

EEG · 16 ch512 HzBIDS 1.9.0Task · rstateHealthyAuditoryResting-state

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 NM000341

dataset = NM000341(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 = NM000341(cache_dir="./data", subject="01")

Advanced query

dataset = NM000341(
    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{nm000341,
  title = {Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD)},
  author = {G. Cattan and P. L. C. Rodrigues and M. Congedo},
  doi = {10.5281/zenodo.2617084},
  url = {https://doi.org/10.5281/zenodo.2617084},
}

§ 02Study · The README

About This Dataset#

Passive Head Mounted Display with Music Listening dataset [1]_.

Code: Cattan2019-PHMD

Paradigm: rstate DOI: 10.5281/zenodo.2617084 Subjects: 12 Sessions per subject: 1 Events: off=1, on=2 Trial interval: [0, 1] s File format: mat and csv

Cattan2019-PHMD

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_1020 Hardware: g.USBamp
View full README
Cattan2019-PHMD

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_1020 Hardware: g.USBamp Software: OpenViBE Reference: right earlobe Ground: AFz Sensor type: wet Line frequency: 50.0 Hz Online filters: no digital filter Cap manufacturer: EasyCap Cap model: EC20 Electrode type: wet

Participants

Number of subjects: 12 Health status: healthy Age: mean=26.25, std=2.63 Gender distribution: male=9, female=3 Species: human

Experimental Protocol

Paradigm: rstate Number of classes: 2 Class labels: off, on Trial duration: 60.0 s Study design: focus on the marker and to listen to the music that was diffused during the experiment (Bach Invention from one to ten on harpsichord). Feedback type: none Stimulus type: visual fixation marker Stimulus modalities: visual, auditory Primary modality: auditory Training/test split: False Instructions: Subjects were asked to focus on the marker and to listen to the music that was diffused during the experiment

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser off
     ├─ Experiment-structure
     └─ Rest

on
├─ Experiment-structure
└─ Rest
Data Structure

Blocks per session: 10 Block duration: 60.0 s Trials context: 5 blocks with smartphone switched-off and 5 blocks with smartphone switched-on, randomized sequence

Preprocessing

Data state: raw, unfiltered Preprocessing applied: False Notes: Data were acquired with no digital filter. No Faraday cage used to mimic real-world usage.

BCI Application

Applications: vr_ar Environment: laboratory Online feedback: False

Tags

Pathology: Healthy Modality: EEG Type: Resting State

Documentation

Description: This dataset contains electroencephalographic recordings of 12 subjects listening to music with and without a passive head-mounted display DOI: 10.5281/zenodo.2617084 Associated paper DOI: 10.2312/vriphys.20181064 License: CC-BY-4.0 Investigators: G. Cattan, P. L. C. Rodrigues, M. Congedo Senior author: M. Congedo Institution: GIPSA-lab, CNRS, University Grenoble-Alpes, Grenoble INP Address: GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402, France Country: FR Repository: Zenodo Data URL: https://doi.org/10.5281/zenodo.2617084 Publication year: 2019 How to acknowledge: Python code for manipulating the data is available at plcrodrigues/py.PHMDML.EEG.2017-GIPSA Keywords: Electroencephalography (EEG), Virtual Reality (VR), Passive Head-Mounted Display (PHMD), experiment

Abstract

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2617084 in mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings of 12 subjects listening to music with and without a passive head-mounted display, that is, a head-mounted display which does not include any electronics at the exception of a smartphone. The electroencephalographic headset consisted of 16 electrodes. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017. Python code for manipulating the data is available at plcrodrigues/py.PHMDML.EEG.2017-GIPSA. The ID of this dataset is PHMDML.EEG.2017-GIPSA.

Methodology

Subjects sat in front of screen at ~50 cm distance without instrumental noise-reduction devices. EEG cap and Samsung Gear were placed on subject. Smartphones were continuously swapped between switched-on and switched-off conditions. Each block consisted of 1 minute of EEG recording with eyes opened. The sequence of 10 blocks was randomized prior to experiment using random number generator with no autocorrelation. Triggers marked beginning of each block (1=switched-off, 2=switched-on).

References

Cattan, P. L. Coelho Rodrigues, and M. Congedo, ‘Passive Head-Mounted Display Music-Listening EEG dataset’, Gipsa-Lab ; IHMTEK, Research Report 2, Mar. 2019. doi: 10.5281/zenodo.2617084.

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=12, range 26–26 yr, mean 26.0 yr)

25

Other · 12

Channel counts: 16 ch (n=12 recordings)

Sampling frequencies: 512.0 Hz (n=12 recordings)

Total recording duration: 2 h 44 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 512 Hz · 12 subjects, 12 recordings

Live trace viewer — sub-1 · ses-0 · task-rstate · run-0

Showing one representative recording out of 12 subjects and 12 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 · 16 sensors — 16 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.NM000341(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Cattan2019_PHMD

Canonical—

Importable asNM000341 · Cattan2019_PHMD

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

class eegdash.dataset.NM000341(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD)

Study:: nm000341 (NeMAR)
Author (year):: Cattan2019_PHMD
Canonical:: —

Also importable as: NM000341, Cattan2019_PHMD.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. Subjects: 12; recordings: 12; 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/nm000341 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000341 DOI: https://doi.org/10.5281/zenodo.2617084

Examples

>>> from eegdash.dataset import NM000341
>>> dataset = NM000341(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 — NM000341.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 nm000341 to reproduce the tutorial on this dataset.

Citation

G. Cattan, P. L. C. Rodrigues, M. Congedo (2019). Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD). 10.5281/zenodo.2617084

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.5281/zenodo.2617084.

Related & sibling datasets

NM000189EEG · 10 subj NM000190EEG · 10 subj NM000148EEG · 30 subj NM000192EEG · 11 subj NM000234EEG · 21 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.5281/zenodo.2617084

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000341`
Title	Cattan, Rodrigues & Congedo 2019 — Passive Head-Mounted Display Music-Listening EEG dataset (PHMD)
Author (year)	`Cattan2019_PHMD`
Canonical	—
Importable as	`NM000341`, `Cattan2019_PHMD`
Year	2019
Authors	Cattan, P. L. C. Rodrigues, M. Congedo
License	CC-BY-4.0
Citation / DOI	doi:10.5281/zenodo.2617084
Source links	OpenNeuro \| NeMAR \| Source URL

NM000341: eeg dataset, 12 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#