EEGdash›NeMAR›ON002720
Iss. 2720 · 18 subjects · 165 recordings · CC0
Dataset Brief · A dataset recorded during development of a tempo-based brain-…

ON002720: eeg dataset, 18 subjects#

A dataset recorded during development of a tempo-based brain-computer music interface

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 a tempo-based brain-computer music interface. 10.82901/nemar.on002720

Modality: eeg Subjects: 18 Recordings: 165 License: CC0 Source: nemar

Metadata: Complete (100%)

18-participant EEG dataset — A dataset recorded during development of a tempo-based brain-computer music interface.

Data & curation Ian Daly · Nicoletta Nicolaou · Duncan Williams · Faustina Hwang · Alexis Kirke · Eduardo Miranda · …
Senior author Slawomir J. Nasuto
Year 2018 · Distributed via NeMAR
EEG · 19 ch1000 HzBIDS 1.0.2
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 ON002720

dataset = ON002720(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = ON002720(cache_dir="./data", subject="01")

Advanced query

dataset = ON002720(
    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{on002720,
  title = {A dataset recorded during development of a tempo-based brain-computer music interface},
  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.on002720},
  url = {https://doi.org/10.82901/nemar.on002720},
}
§ 02Study · The README

About This Dataset#

  1. Project

  1. Dataset

  2. Terms of Use

  3. Contents

  4. 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

DOI

0. Sections

2. DATASET

Title: EEG from a Brain-Computer Music Interface for controlling music tempo.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2014a; 2014b) (please see Section 5 for full references). The dataset is obtained from a music-based Brain-Computer Interface constructed to allow users to modulate the tempo of a piece of music dynamically via intentional control. The dataset contains the electroencephalogram (EEG) data from 19 healthy participants instructed to increase the tempo of the music via kinaesthetically imagining squeezing a ball in their right hand or decrease the tempo by relaxing. The paradigm was split into 9 runs. The first was a calibration run, containing 30 trials in pairs of increase and decrease tempo trials. Publication Year: 2018

View full README

DOI

0. Sections

2. DATASET

Title: EEG from a Brain-Computer Music Interface for controlling music tempo.

Description: This dataset accompanies the publication by Daly et al. (2018) and has been analysed in Daly et al. (2014a; 2014b) (please see Section 5 for full references). The dataset is obtained from a music-based Brain-Computer Interface constructed to allow users to modulate the tempo of a piece of music dynamically via intentional control. The dataset contains the electroencephalogram (EEG) data from 19 healthy participants instructed to increase the tempo of the music via kinaesthetically imagining squeezing a ball in their right hand or decrease the tempo by relaxing. The paradigm was split into 9 runs. The first was a calibration run, containing 30 trials in pairs of increase and decrease tempo trials. 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

Zip File listing:

The dataset comprises data from 19 subjects.

The data is provided in BIDS format. The sampling rate is 1 kHz and the EEG corresponding to a music clip is 20 s long (the duration of the clips).

5. METHOD and PROCESSING

This information is available in the following publications: [1] Daly, I., … “”, Dataset paper, 2018. [2] Daly, I., Hallowell, J., Hwang, F., Kirke, A., Malik, A., Roesch, E., Weaver, J., Williams, D., Miranda, E. R., Nasuto, S. J., “Changes in music tempo entrain movement related brain activity”, in Proc. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’14), Chicago, Illinois, USA; pp. , 2014a. [3] Daly, I., Williams, D., Hwang, F., Kirke, A., Malik, A., Roesch, E., Weaver, J., Miranda, E. R., Nasuto, S. J., “Brain-computer music interfacing for continuous control of musical tempo”, in Proc. 6th International Brain-Computer Interface Conference 2014, Graz, Austria; 2014b Please cite these references and the reference to the music generator 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=18, range 18–28 yr, mean 21.7 yr)

152025
Female · 4Male · 14

Sex composition

18
subjects
Female
4
Male
14
F : M ratio
0.29 : 1
22% female · n = 18 subjects with reported sex.

Channel counts: 19 ch (n=165 recordings)

Sampling frequencies: 1000.0 Hz (n=165 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 19 ch · EEG · 1000 Hz · 18 subjects, 165 recordings
Live trace viewer — sub-07

Showing one representative recording out of 18 subjects and 165 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.

NEMAR pipeline statistics — ON002720
HED event descriptors word cloud HED event descriptors word cloud — ON002720
§ 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

ON002720

Title

A dataset recorded during development of a tempo-based brain-computer music interface

Author (year)

—

Canonical

—

Importable as

ON002720

Year

2018

Authors

Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto

License

CC0

Citation / DOI

10.82901/nemar.on002720

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{on002720,
  title = {A dataset recorded during development of a tempo-based brain-computer music interface},
  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.on002720},
  url = {https://doi.org/10.82901/nemar.on002720},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.ON002720(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asON002720
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.ON002720(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A dataset recorded during development of a tempo-based brain-computer music interface

Study:

on002720 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: ON002720.

Modality: eeg; Subject type: Unknown. Subjects: 18; recordings: 165; 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/on002720 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on002720 DOI: https://doi.org/10.82901/nemar.on002720

Examples

>>> from eegdash.dataset import ON002720
>>> dataset = ON002720(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 — ON002720.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 on002720 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 a tempo-based brain-computer music interface. 10.82901/nemar.on002720

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on002720.

BIDS
BIDS 1.0.2
Sidecars
events · events.json · channels · eeg.json
Provenance
CC0 · 10.82901/nemar.on002720
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#