DS003570: eeg dataset, 40 subjects#
EEG: Improvisation and Musical Structures
Citation: Andrew Goldman, Tyreek Jackson, Paul Sajda (—). EEG: Improvisation and Musical Structures. 10.18112/openneuro.ds003570.v1.0.0
40-participant EEG dataset — EEG: Improvisation and Musical Structures.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003570
dataset = DS003570(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003570(cache_dir="./data", subject="01")
Advanced query
dataset = DS003570(
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{ds003570,
title = {EEG: Improvisation and Musical Structures},
author = {Andrew Goldman and Tyreek Jackson and Paul Sajda},
doi = {10.18112/openneuro.ds003570.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003570.v1.0.0},
}
About This Dataset#
The musicians were instructed to listen to chord progressions, that each consisted of three chords. We refer to one instance of such a progression in the recording as a trial. Every one of the three chords in one trial sounded in sequence, each for 400 ms in piano timbre, after which each trial ended with another 400 ms silence. This resulted in a fixed, total trial length of 1600 ms. The only progressions used in the experiment were ii-IV-I, ii-V-I, ii-IV6-I and ii-V6-I. Each experimental block consisted of 180 trials. For each such block one of the four aforementioned progressions were chosen as “standard”, resulting in four types of blocks. These “block types” were used to counterbalance the effect of other features of the individual progressions such as intervallic content that may have been in themselves salient. An experimental block always started with at least eight “standard” trials for the purpose of allowing participants to learn what type of progression would be the standard for the current block. There were two types of deviant trials that each occurred at a probability of 7.5% (in total 15%). Every deviant trial was followed by at least three standard trials. Deviant trials only differed from standard trials in terms of the middle chord: (1) Exemplar deviants, where the middle chord was replaced with a chord of identical notes but different inversion. For example, if the middle chord for a standard trial in that experimental block was V then the middle chord for the exemplar deviant in that block would be V6. For (2) function deviants, the middle chord was replaced by a chord from a different functional class. For example, if the middle chord for a standard was again V, then the middle chord for the corresponding function deviant in that block would be IV. Importantly, the key for each trial’’s chord progression was picked at random. This meant that musicians needed to examine the second chord of every trial relative to the first and/or third to identify whether the trial was a standard or deviant. The order of standards and deviants within every one of the four types of experimental blocks was generated once only, and was thus identical across subjects within these block types. For the experiment, every one of the block types occurred twice, thus resulting in a total of eight blocks per subject. The order of the eight blocks was shuffled for every subject. In total, there were 1440 trials per subject of which 222 were functional and 218 were exemplar deviants.
Cohort#
Dataset Statistics#
Age distribution by gender (n=40, range 18–42 yr, mean 25.3 yr)
Sex composition
Channel counts: 64 ch (n=40 recordings)
Sampling frequencies: 2048.0 Hz (n=40 recordings)
Total recording duration: 24 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-AuditoryOddballChords
Showing one representative recording out of
40 subjects and 40 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 · 64 sensors — 64 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 |
EEG: Improvisation and Musical Structures |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Andrew Goldman, Tyreek Jackson, Paul Sajda |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003570,
title = {EEG: Improvisation and Musical Structures},
author = {Andrew Goldman and Tyreek Jackson and Paul Sajda},
doi = {10.18112/openneuro.ds003570.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003570.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003570 · Goldman2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003570(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
EEG: Improvisation and Musical Structures
- Study:
ds003570(OpenNeuro)- Author (year):
Goldman2021- Canonical:
—
Also importable as:
DS003570,Goldman2021.Modality:
eeg. Subjects: 40; recordings: 40; 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/ds003570 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003570 DOI: https://doi.org/10.18112/openneuro.ds003570.v1.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS003570 >>> dataset = DS003570(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/ds003570").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 ds003570 to reproduce the tutorial on this dataset.
Citation
Andrew Goldman, Tyreek Jackson, Paul Sajda (n.d.). EEG: Improvisation and Musical Structures. 10.18112/openneuro.ds003570.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.ds003570.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset