DS006735#

Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding

Access recordings and metadata through EEGDash.

Citation: Tong Shan, Edmund C. Lalor, Ross K. Maddox (2025). Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding. 10.18112/openneuro.ds006735.v2.0.0

Modality: eeg Subjects: 27 Recordings: 220 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006735

dataset = DS006735(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 = DS006735(cache_dir="./data", subject="01")

Advanced query

dataset = DS006735(
    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{ds006735,
  title = {Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding},
  author = {Tong Shan and Edmund C. Lalor and Ross K. Maddox},
  doi = {10.18112/openneuro.ds006735.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006735.v2.0.0},
}

About This Dataset#

Details related to access to the data

Please contact the following authors for further information:

Tong Shan (email: tongshan@stanford.edu)

Ross K. Maddox (email: rkmaddox@med.umich.edu)

Dataset Information#

Dataset ID	`DS006735`
Title	Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding
Year	2025
Authors	Tong Shan, Edmund C. Lalor, Ross K. Maddox
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006735.v2.0.0
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: 27
Recordings: 220
Tasks: 1

Channels & sampling rate

Channels: 36 (48), 63 (4), 34 (2)
Sampling rate (Hz): 10000.0
Duration (hours): 0.0

API Reference#

Use the DS006735 class to access this dataset programmatically.

class eegdash.dataset.DS006735(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006735. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 27; recordings: 27; 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/ds006735 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006735

Examples

>>> from eegdash.dataset import DS006735
>>> dataset = DS006735(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