EEGdash›OpenNeuro›DS002723

Iss. 2723 · 8 subjects · 44 recordings · CC0

Dataset Brief · A dataset recorded during development of an affective brain-c…

DS002723: eeg dataset, 8 subjects#

A dataset recorded during development of an affective brain-computer music interface: testing session

Citation: Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto (2018). A dataset recorded during development of an affective brain-computer music interface: testing session. 10.18112/openneuro.ds002723.v1.1.0

8-participant EEG dataset — A dataset recorded during development of an affective brain-computer music interface: testing session.

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

EEG · 37 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 DS002723

dataset = DS002723(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 = DS002723(cache_dir="./data", subject="01")

Advanced query

dataset = DS002723(
    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{ds002723,
  title = {A dataset recorded during development of an affective brain-computer music interface: testing session},
  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.ds002723.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds002723.v1.1.0},
}

§ 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

0. Sections

2. DATASET

EEG data from an affective Music Brain-Computer: online real-time control.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2016) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to investigate the performance of a real-time and online brain-computer interface that identified the user’s emotional state and modified music on-the-fly in order to induce a target emotional state. For this purpose, participants listened to 60 s music clips targeting different affective states, as defined by valence and arousal. The music clips were generated using a synthetic music generator. The dataset contains the EEG data from 8 healthy adult participants during real-time control of the system while listening to the music clips, together with the reported affective state (valence and arousal values). This dataset is connected to 2 additional datasets: 1. EEG data from an affective Music Brain-Computer Interface: system calibration. doi: 2. EEG data from an affective Music Brain-Computer: offline training data to induce target emotional states. doi:

View full README

0. Sections

2. DATASET

EEG data from an affective Music Brain-Computer: online real-time control.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2016) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to investigate the performance of a real-time and online brain-computer interface that identified the user’s emotional state and modified music on-the-fly in order to induce a target emotional state. For this purpose, participants listened to 60 s music clips targeting different affective states, as defined by valence and arousal. The music clips were generated using a synthetic music generator. The dataset contains the EEG data from 8 healthy adult participants during real-time control of the system while listening to the music clips, together with the reported affective state (valence and arousal values). This dataset is connected to 2 additional datasets: 1. EEG data from an affective Music Brain-Computer Interface: system calibration. doi: 2. EEG data from an affective Music Brain-Computer: offline training data to induce target emotional states. doi:

Please note that the number of participants varies between datasets; however, participant codes are the same across all three datasets.

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

Contributors: Isil Poyraz Bilgin, James Weaver, Asad Malik, Alexis Kirke, Duncan Williams. 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 synthetic generator used to generate the music clips was presented in Williams et al., “Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System”, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005.

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

The dataset comprises data from 8 subjects. The sampling rate is 1 kHz and the music listening task corresponding to a music clip is 60 s long (clip duration). During the first 20 s, the music clip places the listener in emotional state A, while for the remaining 40 s the music clip targets the affective trajectory from emotional state B to C.

Within a 60s music listening epoch there are two target affective states. In the first 20s the music is generated to target one affective state (target A), for the next 20s the BCMI attempts to (a) work out what affective state the participant is actually in, and (b) generate music to move them from this affective state to the next targetted affective state (target B), which is targetted for the last 20s of the 60s music listening epoch.

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., “Neural and physiological data from participants listening to affective music”, Scientific Data, 2018. [2] Daly, I., Williams, D., Hwang, F., Kirke, A., Malik, A., Weaver, J., Miranda, E. R., Nasuto, S. J., “Affective Brain-Computer Music Interfacing”, Journal of Neural Engineering, 13:4, July 2016. http://dx.doi.org/10.1088/1741-2560/13/4/046022 If you use this dataset in your study please cite these references, as well as the following reference: [3] Williams, D., Kirke, A., Miranda, E.R., Daly, I., Hwang, F., Weaver, J., Nasuto, S.J., “Affective Calibration of Musical Feature Sets in an Emotionally Intelligent Music Composition System”, ACM Trans. Appl. Percept. 14, 3, Article 17 (May 2017), 13 pages. DOI: https://doi.org/10.1145/3059005 Thank you for your interest in our work.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=8, range 19–30 yr, mean 22.5 yr)

152030

Female · 6Male · 2

Sex composition

Female
6
Male
2
F : M ratio
3.00 : 1

75% female · n = 8 subjects with reported sex.

Channel counts: 37 ch (n=44 recordings)

Sampling frequencies: 1000.0 Hz (n=44 recordings)

Total recording duration: 10 h 28 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 37 ch · EEG · 1000 Hz · 8 subjects, 44 recordings

Live trace viewer — sub-13

Showing one representative recording out of 8 subjects and 44 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 · 32 sensors — 32 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.DS002723(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Daly2020_session

Canonical—

Importable asDS002723 · Daly2020_session

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

class eegdash.dataset.DS002723(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A dataset recorded during development of an affective brain-computer music interface: testing session

Study:: ds002723 (OpenNeuro)
Author (year):: Daly2020_session
Canonical:: —

Also importable as: DS002723, Daly2020_session.

Modality: eeg. Subjects: 8; recordings: 44; 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/ds002723 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002723 DOI: https://doi.org/10.18112/openneuro.ds002723.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS002723
>>> dataset = DS002723(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/ds002723 · pull with datasets.load_dataset("EEGDash/ds002723").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS002723.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 ds002723 to reproduce the tutorial on this dataset.

Citation

Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, … (2018). A dataset recorded during development of an affective brain-computer music interface: testing session. 10.18112/openneuro.ds002723.v1.1.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds002723.v1.1.0.

Related & sibling datasets

DS003626EEG · 10 subj DS004840EEG · 9 subj DS002724EEG · 10 subjSame authors DS004018EEG · 16 subj DS004357EEG · 16 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds002723.v1.1.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS002723`
Title	A dataset recorded during development of an affective brain-computer music interface: testing session
Author (year)	`Daly2020_session`
Canonical	—
Importable as	`DS002723`, `Daly2020_session`
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.ds002723.v1.1.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS002723: eeg dataset, 8 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#