DS003774#

Music Listening- Genre EEG dataset (MUSIN-G)

Access recordings and metadata through EEGDash.

Citation: Krishna Prasad Miyapuram, Pankaj Pandey, Nashra Ahmad, Bharatesh R Shiraguppi, Esha Sharma, Prashant Lawhatre, Dhananjay Sonawane, Derek Lomas (2021). Music Listening- Genre EEG dataset (MUSIN-G). 10.18112/openneuro.ds003774.v1.0.2

Modality: eeg Subjects: 20 Recordings: 1685 License: CC0 Source: openneuro Citations: 8.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003774

dataset = DS003774(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 = DS003774(cache_dir="./data", subject="01")

Advanced query

dataset = DS003774(
    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{ds003774,
  title = {Music Listening- Genre EEG dataset (MUSIN-G)},
  author = {Krishna Prasad Miyapuram and Pankaj Pandey and Nashra Ahmad and Bharatesh R Shiraguppi and Esha Sharma and Prashant Lawhatre and Dhananjay Sonawane and Derek Lomas},
  doi = {10.18112/openneuro.ds003774.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003774.v1.0.2},
}

About This Dataset#

The dataset contains Electroencephalography (EEG) responses from 20 Indian participants, on 12 songs of different genres (from Indian Classical to Goth Rock). Each session indicates a song by its number.

For the experiment, the participants were indicated to close their eyes indicated by a single beep, and the song was presented to them on speakers. After listening to each song, a double beep was presented, asking them to open their eyes and rate their familiarity and enjoyment to the song. The responses were taken on a scale of 1 to 5, where 1 meant most familiar or most enjoyable, and 5 meant least familiar or least enjoyable.

The events timeline in segmented data must be ignored as these are inherited from rawdata

Dataset Information#

Dataset ID	`DS003774`
Title	Music Listening- Genre EEG dataset (MUSIN-G)
Year	2021
Authors	Krishna Prasad Miyapuram, Pankaj Pandey, Nashra Ahmad, Bharatesh R Shiraguppi, Esha Sharma, Prashant Lawhatre, Dhananjay Sonawane, Derek Lomas
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003774.v1.0.2
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: 20
Recordings: 1685
Tasks: 1

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 1000.0 (264), 250.0 (216)
Duration (hours): 0.0

API Reference#

Use the DS003774 class to access this dataset programmatically.

class eegdash.dataset.DS003774(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003774. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 20; recordings: 240; tasks: 1.

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/ds003774 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003774

Examples

>>> from eegdash.dataset import DS003774
>>> dataset = DS003774(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