EEGdash›OpenNeuro›DS002721

Iss. 2721 · 31 subjects · 185 recordings · CC0

Dataset Brief · An EEG dataset recorded during affective music listening

DS002721: eeg dataset, 31 subjects#

An EEG dataset recorded during affective music listening

Citation: Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto (2018). An EEG dataset recorded during affective music listening. 10.18112/openneuro.ds002721.v1.0.2

31-participant EEG dataset — An EEG dataset recorded during affective music listening.

Data & curation Ian Daly · Nicoletta Nicolaou · Duncan Williams · Faustina Hwang · Alexis Kirke · Eduardo Miranda · …
Year 2018 · Distributed via OpenNeuro

EEG · 19 ch1000 HzBIDS 1.0.2HealthyAuditoryAffect

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 DS002721

dataset = DS002721(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 = DS002721(cache_dir="./data", subject="01")

Advanced query

dataset = DS002721(
    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{ds002721,
  title = {An EEG dataset recorded during affective music listening},
  author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},
  doi = {10.18112/openneuro.ds002721.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds002721.v1.0.2},
}

§ 02Study · The README

About This Dataset#

Project

Dataset
Terms of Use
Contents
Method and Processing

Title: Brain-Computer Music Interface for Monitoring and Inducing Affective States (BCMI-MIdAS)

Dates: 2012-2017 Funding organisation: Engineering and Physical Sciences Research Council (EPSRC) Grant no.: EP/J003077/1 and EP/J002135/1.

0. Sections

2. DATASET

Title: EEG data investigating neural correlates of music-induced emotion.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2014; 2015a; 2015b) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to investigate the EEG neural correlates of music-induced emotion. For this purpose 31 healthy adult participants listened to 40 music clips of 12 s duration each, targeting a range of emotional states. The music clips comprised excerpts from film scores spanning a range of styles and rated on induced emotion. The dataset contains unprocessed EEG data from all 31 participants (age range 18-66, 18 female) while listening to the music clips, together with the reported induced emotional responses . The paradigm involved 6 runs of EEG recordings. The first and last runs were resting state runs, during which participants were instructed to sit still and rest for 300 s. The other 4 runs each contained 10 music listening trials.

Publication Year: 2018

View full README

0. Sections

2. DATASET

Title: EEG data investigating neural correlates of music-induced emotion.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2014; 2015a; 2015b) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to investigate the EEG neural correlates of music-induced emotion. For this purpose 31 healthy adult participants listened to 40 music clips of 12 s duration each, targeting a range of emotional states. The music clips comprised excerpts from film scores spanning a range of styles and rated on induced emotion. The dataset contains unprocessed EEG data from all 31 participants (age range 18-66, 18 female) while listening to the music clips, together with the reported induced emotional responses . The paradigm involved 6 runs of EEG recordings. The first and last runs were resting state runs, during which participants were instructed to sit still and rest for 300 s. The other 4 runs each contained 10 music listening trials.

Publication Year: 2018 Creator: Nicoletta Nicolaou, Ian Daly.

Contributors: Isil Poyraz Bilgin, James Weaver, Asad Malik. Principal Investigator: Slawomir Nasuto (EP/J003077/1).

Co-Investigator: Eduardo Miranda (EP/J002135/1). Organisation: University of Reading Rights-holders: University of Reading Source: The musical stimuli were taken from Eerola & Vuoskoski, “A comparison of the discrete and dimensional models of emotion in music”, Psychol. Music, 39:18-49, 2010 (doi: 10.1177/0305735610362821). Stimuli set 1 was used (https://www.jyu.fi/hytk/fi/laitokset/mutku/en/research/projects2/past-projects/coe/materials/emotion/soundtracks/set1/view) System: The data is prepared for use on Windows systems and no garanantee is made that the datasets can be opened correctly on other systems.

3. TERMS OF USE

Copyright University of Reading, 2018. This dataset is licensed by the rights-holder(s) under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/.

4. CONTENTS

BIDS File listing:

The dataset comprises data from 31 participants, named using the convention: sub_s_number where: s_number is a random participant number from 1 to 31. For example: ‘sub-08’ contains data obtained from participant 8.

The data is BIDS format and contains EEG and associated meta data. The sampling rate is 1 kHz and the EEG corresponding to a music clip is 20 s long (the duration of the clips). Each data folder contains the following data (please note that the number of runs varies between participants):

5. METHOD and PROCESSING

This information is available in the following publications: [1] Daly, I., Nicolaou, N., Williams, D., Hwang, F., Kirke, A., Miranda, E., Nasuto, S.J., Ԏeural and physiological data from participants listening to affective musicԬ Scientific Data, 2018. [2] Daly, I., Malik, A., Hwang, F., Roesch, E., Weaver, J., Kirke, A., Williams, D., Miranda, E. R., Nasuto, S. J., Ԏeural correlates of emotional responses to music: an EEG studyԬ Neuroscience Letters, 573: 52-7, 2014; doi: 10.1016/j.neulet.2014.05.003. [3] Daly, I., Hallowell, J., Hwang, F., Kirke, A., Malik, A., Roesch, E., Weaver, J., Williams, D., Miranda, E., Nasuto, S.J., ԃhanges in music tempo entrain movement related brain activityԬ Proc. IEEE EMBC 2014, pp.4595-8; doi: 10.1109/EMBC.2014.6944647 [4] Daly, I., Williams, D., Hallowell, J., Hwang, F., Kirke, A., Malik, A., Weaver, J., Miranda, E., Nasuto, S.J., ԍusic-induced emotions can be predicted from a combination of brain activity and acoustic featuresԬ Brain and Cognition, 101:1-11, 2015b; doi: 10.1016/j.bandc.2015.08.003 Please cite these references if you use this dataset in your study.

Thank you for your interest in our work.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=31, range 18–66 yr, mean 39.1 yr)

1520253035404550556065

Female · 18Male · 13

Sex composition

Female
18
Male
13
F : M ratio
1.38 : 1

58% female · n = 31 subjects with reported sex.

Channel counts: 19 ch (n=185 recordings)

Sampling frequencies: 1000.0 Hz (n=185 recordings)

Total recording duration: 26 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 19 ch · EEG · 1000 Hz · 31 subjects, 185 recordings

Live trace viewer — sub-13

Showing one representative recording out of 31 subjects and 185 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 · 19 sensors — 19 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.DS002721(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Daly2020_recorded_affective

Canonical—

Importable asDS002721 · Daly2020_recorded_affective

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

class eegdash.dataset.DS002721(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

An EEG dataset recorded during affective music listening

Study:: ds002721 (OpenNeuro)
Author (year):: Daly2020_recorded_affective
Canonical:: —

Also importable as: DS002721, Daly2020_recorded_affective.

Modality: eeg. Subjects: 31; recordings: 185; tasks: 0.

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/ds002721 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002721 DOI: https://doi.org/10.18112/openneuro.ds002721.v1.0.2 NEMAR citation count: 10

Examples

>>> from eegdash.dataset import DS002721
>>> dataset = DS002721(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 FacePre-bundled mirror at EEGDash/ds002721 · pull with datasets.load_dataset("EEGDash/ds002721").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS002721.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 ds002721 to reproduce the tutorial on this dataset.

Citation

Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, … (2018). An EEG dataset recorded during affective music listening. 10.18112/openneuro.ds002721.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds002721.v1.0.2.

Related & sibling datasets

DS005289EEG · 39 subj DS002720EEG · 18 subjSame authors DS004075EEG · 29 subj DS003555EEG · 30 subj DS004446EEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds002721.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS002721`
Title	An EEG dataset recorded during affective music listening
Author (year)	`Daly2020_recorded_affective`
Canonical	—
Importable as	`DS002721`, `Daly2020_recorded_affective`
Year	2018
Authors	Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto
License	CC0
Citation / DOI	10.18112/openneuro.ds002721.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS002721: eeg dataset, 31 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#