DS003774: eeg dataset, 20 subjects#

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.v1.0.0

Modality: eeg Subjects: 20 Recordings: 240 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.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds003774.v1.0.0},
}

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.

Dataset Information#

Dataset ID	`DS003774`
Title	Music Listening- Genre EEG dataset (MUSIN-G)
Author (year)	`Miyapuram2021`
Canonical	—
Importable as	`DS003774`, `Miyapuram2021`
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	10.18112/openneuro.ds003774.v1.0.0
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: 20
Recordings: 240
Tasks: 1

Channels & sampling rate

Channels: 129
Sampling rate (Hz): 1000.0 (132), 250.0 (108)
Duration (hours): 8.63974111111111

Electrode Layout#

Electrode layout — EEG · 129 sensors — 129 channels

Dataset Statistics#

Channel counts: 129 ch (n=240 recordings)

Sampling frequencies (Hz)

2501000

Total recording duration: 8 h 38 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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

Music Listening- Genre EEG dataset (MUSIN-G)

Study:: ds003774 (OpenNeuro)
Author (year):: Miyapuram2021
Canonical:: —

Also importable as: DS003774, Miyapuram2021.

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 DOI: https://doi.org/10.18112/openneuro.ds003774.v1.0.0 NEMAR citation count: 8

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: 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)