DS003774: eeg dataset, 20 subjects#
Music Listening- Genre EEG dataset (MUSIN-G)
Citation: Krishna Prasad Miyapuram, Pankaj Pandey, Nashra Ahmad, Bharatesh R Shiraguppi, Esha Sharma, Prashant Lawhatre, Dhananjay Sonawane, Derek Lomas (—). Music Listening- Genre EEG dataset (MUSIN-G). 10.18112/openneuro.ds003774.v1.0.0
20-participant EEG dataset — Music Listening- Genre EEG dataset (MUSIN-G).
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Channel counts: 129 ch (n=240 recordings)
Sampling frequencies (Hz)
Total recording duration: 8 h 38 min
Signal · Electrodes & live trace#
Live trace viewer — sub-019 · ses-10 · task-MusicListening · run-10
Showing one representative recording out of
20 subjects and 240 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 · 129 sensors — 129 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 |
Music Listening- Genre EEG dataset (MUSIN-G) |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Krishna Prasad Miyapuram, Pankaj Pandey, Nashra Ahmad, Bharatesh R Shiraguppi, Esha Sharma, Prashant Lawhatre, Dhananjay Sonawane, Derek Lomas |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003774 · Miyapuram2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003774(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
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/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)
- 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.pytorchdatasets.load_dataset("EEGDash/ds003774").huggingface
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 ds003774 to reproduce the tutorial on this dataset.
Citation
Krishna Prasad Miyapuram, Pankaj Pandey, Nashra Ahmad, Bharatesh R Shiraguppi, Esha Sharma, … (n.d.). Music Listening- Genre EEG dataset (MUSIN-G). 10.18112/openneuro.ds003774.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003774.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset