DS002723#

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

Access recordings and metadata through EEGDash.

Citation: Ian Daly, Nicoletta Nicolaou, Duncan Williams, Faustina Hwang, Alexis Kirke, Eduardo Miranda, Slawomir J. Nasuto (2020). A dataset recorded during development of an affective brain-computer music interface: testing session. 10.18112/openneuro.ds002723.v1.1.0

Modality: eeg Subjects: 8 Recordings: 204 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

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},
}

About This Dataset#

0. Sections

Project
Dataset
Terms of Use
Contents
Method and Processing

1. PROJECT

View full README

0. Sections

Project
Dataset
Terms of Use
Contents
Method and Processing

1. PROJECT

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

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.

Dataset Information#

Dataset ID	`DS002723`
Title	A dataset recorded during development of an affective brain-computer music interface: testing session
Year	2020
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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 8
Recordings: 204
Tasks: 1

Channels & sampling rate

Channels: 37 (44), 32 (44)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS002723 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds002723. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 8; recordings: 44; tasks: 6.

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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None