EEGdash›NeMAR›ON008701
Iss. 8701 · 20 subjects · 399 recordings · CC0
Dataset Brief · MET - Music-Induced Emotion EEG Dataset

ON008701: eeg dataset, 20 subjects#

MET - Music-Induced Emotion EEG Dataset

Access recordings and metadata through EEGDash.

Citation: Weng Lam, Cheang (2019). MET - Music-Induced Emotion EEG Dataset. 10.82901/nemar.on008701

Modality: eeg Subjects: 20 Recordings: 399 License: CC0 Source: nemar

Metadata: Complete (100%)

20-participant EEG dataset — MET - Music-Induced Emotion EEG Dataset.

Data & curation Weng Lam, Cheang
Senior author Weng Lam, Cheang
Year 2019 · Distributed via NeMAR
EEG · 67 ch1000 HzBIDS 1.7.0Task · music20 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#

Install

pip install eegdash

Access the data

from eegdash.dataset import ON008701

dataset = ON008701(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = ON008701(cache_dir="./data", subject="01")

Advanced query

dataset = ON008701(
    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{on008701,
  title = {MET - Music-Induced Emotion EEG Dataset},
  author = {Weng Lam, Cheang},
  doi = {10.82901/nemar.on008701},
  url = {https://doi.org/10.82901/nemar.on008701},
}
§ 02Study · The README

About This Dataset#

The MET (Music-Induced EEG Dataset) contains EEG recordings collected from 20 participants during resting-state and music-listening experiments.

Each participant completed:

  • One resting-state recording session (ses-rest)

  • Twenty music-listening sessions (ses-t01 to ses-t20)

DOI

MET: Music-Induced EEG Dataset

Overview

Each music session corresponds to a different song. The total recording duration for each song is 130 seconds, including: - 5 seconds before music onset - 120 seconds of music playback

View full README

DOI

MET: Music-Induced EEG Dataset

Overview

Each music session corresponds to a different song. The total recording duration for each song is 130 seconds, including: - 5 seconds before music onset - 120 seconds of music playback - 5 seconds after music offset

EEG signals were originally recorded using a 128-channel EEG system at a sampling rate of 1000 Hz. After preprocessing and channel selection, 66 EEG channels are provided in this dataset.

The dataset follows the Brain Imaging Data Structure (BIDS) specification for EEG recordings.

Repository Structure

Each EEG session contains the following files: *_eeg.eeg: EEG signal data in BrainVision format. (Unit: µV [microvolts]) *_eeg.vhdr: BrainVision header file containing acquisition parameters. *_eeg.vmrk: Event marker file. *_eeg.json: EEG recording metadata. *_channels.tsv: Information about EEG channels, including channel names and units. *_events.tsv: Timing information for experimental events. *_events.json: Description of event variables stored in events.tsv. *_electrodes.tsv: Spatial coordinates of EEG electrodes. *_coordsystem.json: Coordinate system used for electrode locations.

*_scans.tsv: List of files acquired during the session.

Sample Code (PTTHON):

from mne_bids import BIDSPath, read_raw_bids

1. Set file path and file name

bids_path = BIDSPath(

subject=’01’, # subject index (eg. 01) session=’t01’, # trial index (eg. t01) task=’music’, datatype=’eeg’, root=’/Users/kathy/Documents/EEG data/EGI_preprocessed_bids’ # directory root path

)

2. Load BIDS file

raw_read = read_raw_bids(bids_path=bids_path, extra_params={‘preload’: True})

3. Check Info

print(raw_read.info)

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, 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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 67 ch (n=399 recordings)

Sampling frequencies: 1000.0 Hz (n=399 recordings)

Total recording duration: 14 h 31 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 67 ch · EEG · 1000 Hz · 20 subjects, 399 recordings
Live trace viewer — sub-07 · ses-t25 · task-music

Showing one representative recording out of 20 subjects and 399 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 · 66 sensors — 66 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.

NEMAR pipeline statistics — ON008701
HED event descriptors word cloud HED event descriptors word cloud — ON008701
§ 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…
Full dataset metadata table

Dataset ID

ON008701

Title

MET - Music-Induced Emotion EEG Dataset

Author (year)

—

Canonical

—

Importable as

ON008701

Year

2019

Authors

Weng Lam, Cheang

License

CC0

Citation / DOI

10.82901/nemar.on008701

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{on008701,
  title = {MET - Music-Induced Emotion EEG Dataset},
  author = {Weng Lam, Cheang},
  doi = {10.82901/nemar.on008701},
  url = {https://doi.org/10.82901/nemar.on008701},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.ON008701(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asON008701
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.ON008701(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

MET - Music-Induced Emotion EEG Dataset

Study:

on008701 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: ON008701.

Modality: eeg; Subject type: Unknown. Subjects: 20; recordings: 399; 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/on008701 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on008701 DOI: https://doi.org/10.82901/nemar.on008701

Examples

>>> from eegdash.dataset import ON008701
>>> dataset = ON008701(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 — ON008701.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 on008701 to reproduce the tutorial on this dataset.

Citation

Weng Lam, Cheang (2019). MET - Music-Induced Emotion EEG Dataset. 10.82901/nemar.on008701

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on008701.

BIDS
BIDS 1.7.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC0 · 10.82901/nemar.on008701
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#