ON002724: eeg dataset, 10 subjects#
A dataset recorded during development of an affective brain-computer music interface: training sessions
Access recordings and metadata through EEGDash.
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: training sessions. 10.82901/nemar.on002724
Modality: eeg Subjects: 10 Recordings: 96 License: CC0 Source: nemar
Metadata: Complete (100%)
10-participant EEG dataset — A dataset recorded during development of an affective brain-computer music interface: training sessions.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON002724
dataset = ON002724(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON002724(cache_dir="./data", subject="01")
Advanced query
dataset = ON002724(
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{on002724,
title = {A dataset recorded during development of an affective brain-computer music interface: training sessions},
author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},
doi = {10.82901/nemar.on002724},
url = {https://doi.org/10.82901/nemar.on002724},
}
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
EEG data from an affective Music Brain-Computer Interface: offline training to induce target emotional states.
Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2015) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to train a music brain-computer interface system to induce specific affective states for individual users. For this purpose the participants listened to music clips (40 s) targeting two affective states, as defined by valence and arousal. Data were recorded over 3 sessions (separate days), each containing 4 runs (same day) of 18 trials each. The music clips were generated using a synthetic music generator. The dataset contains the electroencephalogram (EEG), galvanic skin response (GSR) and electrocardiogram (ECG) data from 16 healthy participants while listening to the music clips, together with the reported affective state (valence and arousal values) and auxiliary variables. This dataset is connected to 2 additional datasets:
View full README
0. Sections
2. DATASET
EEG data from an affective Music Brain-Computer Interface: offline training to induce target emotional states.
Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2015) (please see Section 5 for full references). The purpose of the research activity in which the data were collected was to train a music brain-computer interface system to induce specific affective states for individual users. For this purpose the participants listened to music clips (40 s) targeting two affective states, as defined by valence and arousal. Data were recorded over 3 sessions (separate days), each containing 4 runs (same day) of 18 trials each. The music clips were generated using a synthetic music generator. The dataset contains the electroencephalogram (EEG), galvanic skin response (GSR) and electrocardiogram (ECG) data from 16 healthy participants while listening to the music clips, together with the reported affective state (valence and arousal values) and auxiliary variables. 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 Interface: online real-time control. 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 17 subjects, stored using the BIDS format. The sampling rate is 1 kHz and the music listening task corresponding to a music clip is 40 s long (clip duration). During the first 20 s, the music clip targets emotional state A, while for the remaining 20 s the music clip targets emotional state B.
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., Roesch, E., Weaver, J., Miranda, E. R., Nasuto, S. J., �Identifying music-induced emotions from EEG for use in brain-computer music interfacing�, in Proc. 4th Workshop on Affective Brain-Computer Interfaces at the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015). Xi�an, China, 21-25 September 2015.
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=10, range 19–24 yr, mean 21.8 yr)
Sex composition
Channel counts: 37 ch (n=96 recordings)
Sampling frequencies: 1000.0 Hz (n=96 recordings)
Signal · Electrodes & live trace#
Live trace viewer — sub-17 · ses-1
Showing one representative recording out of
10 subjects and 96 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
A dataset recorded during development of an affective brain-computer music interface: training sessions |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2018 |
Authors |
Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on002724,
title = {A dataset recorded during development of an affective brain-computer music interface: training sessions},
author = {Ian Daly and Nicoletta Nicolaou and Duncan Williams and Faustina Hwang and Alexis Kirke and Eduardo Miranda and Slawomir J. Nasuto},
doi = {10.82901/nemar.on002724},
url = {https://doi.org/10.82901/nemar.on002724},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON002724(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: training sessions
- Study:
on002724(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON002724.Modality:
eeg; Subject type:Unknown. Subjects: 10; recordings: 96; 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/on002724 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on002724 DOI: https://doi.org/10.82901/nemar.on002724
Examples
>>> from eegdash.dataset import ON002724 >>> dataset = ON002724(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 on002724 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: training sessions. 10.82901/nemar.on002724
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on002724.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset