DS003082#

Auditory Cortex Mapping Dataset

Access recordings and metadata through EEGDash.

Citation: Jonathan Cote, Etienne de Villers-Sidani (2020). Auditory Cortex Mapping Dataset. 10.18112/openneuro.ds003082.v1.0.1

Modality: meg Subjects: 1 Recordings: 62 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003082

dataset = DS003082(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003082(cache_dir="./data", subject="01")

Advanced query

dataset = DS003082(
    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{ds003082,
  title = {Auditory Cortex Mapping Dataset},
  author = {Jonathan Cote and Etienne de Villers-Sidani},
  doi = {10.18112/openneuro.ds003082.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003082.v1.0.1},
}

About This Dataset#

Brainstorm - Auditory Cortex Mapping Dataset

License

This dataset (MEG and MRI data) was collected by Jonathan Cote of the Neuroplasticity and Sensory Biomarking Lab, Montreal Neurological Institute, McGill University, Canada. Its purpose is to serve as a data example to be used with our MEG-based auditory cortex mapping technique. It is presently released in the Public Domain, and is not subject to copyright in any jurisdiction.

View full README

Brainstorm - Auditory Cortex Mapping Dataset

License

This dataset (MEG and MRI data) was collected by Jonathan Cote of the Neuroplasticity and Sensory Biomarking Lab, Montreal Neurological Institute, McGill University, Canada. Its purpose is to serve as a data example to be used with our MEG-based auditory cortex mapping technique. It is presently released in the Public Domain, and is not subject to copyright in any jurisdiction.

We would appreciate though that you reference this dataset in your publications: please acknowledge its authors (Jonathan Cote and Etienne de Villers-Sidani) and cite the mapping technique publication (under review)

This dataset will first be a single subject, but might be expanded up to the 10 participants in the future.

Presentation of the experiment

Experiment

  • One subject, one acquisition run of around 12 minutes

  • Subject stimulated binaurally with intra-aural earphones (air tubes+transducers)

  • The run contains:

    • 1795 iso-intensity pure tones (IIPT)

    • The frequency of these ranges between 100 Hz and 21527 Hz, spaced by 1/4 octave.

  • Random inter-stimulus interval: randomized but averaging at a presentation rate of 3Hz

  • The subject passively listened while looking at a fixation cross

  • Auditory stimuli generated with the Matlab Psychophysics toolbox

MEG acquisition

  • Acquisition at **120000Hz**, with a **CTF 275** system, subject in seating position

  • Recorded at the Montreal Neurological Institute in January 2015

  • Anti-aliasing low-pass filter at 3000Hz, files saved with the 3rd order gradient

  • Recorded channels (340):

    • 1 Trigger channel indicating the presentation times of the audio stimuli: UADC001 (#306)

    • 26 MEG reference sensors (#4-#29)

    • 272 MEG axial gradiometers (#30-#302)

    • 1 ECG bipolar (#303)

    • 2 EOG bipolar (vertical #304, horizontal #305)

    • 3 Unused channels (#1-#3)

  • 3 datasets:

    • **sub-0001_ses-0001_task-mapping_run-01_meg.ds**: Run #1, 653s, 1795 IIPT, sampled at 12000 Hz

    • **sub-emptyroom_ses-0001_emptyroom_run-01_meg.ds**: Empty room recording, 120s long, sampled at 12000 Hz

    • **sub-emptyroom_ses-0001_emptyroom_run-02_meg.ds**: Empty room recording, 120s long, sampled at 2400 Hz

  • Use of the .ds, not the AUX (standard at the MNI) because they are easier to manipulate in FieldTrip

Stimulation delays

  • **Delay #1**: Transmission of the sound.

Between when the sound card plays the sound and when the subject receives the sound in the ears. This is the time it takes for the transducer to convert the analog audio signal into a sound, plus the time it takes to the sound to travel through the air tubes from the transducer to the subject’s ears. This delay cannot be estimated from the recorded signals: before the acquisition, we placed a sound meter at the extremity of the tubes to record when the sound is delivered. Delay between 4.8ms and 5.0ms**(std = 0.08ms). At a sampling rate of 2400Hz, this delay can be considered ** constant, we will not compensate for it. * **Delay #2**: Recording of the signals.

The CTF MEG systems have a constant delay of **4 samples**between the MEG/EEG channels and the analog channels (such as the audio signal UADC001), because of an anti-aliasing filtered that is applied to the first and not the second. This translate here to a ** constant delay**of **1.7ms**. * **Uncorrected delays**: We will keep the delays. We decide not to compensate for these delays because they do not introduce any jitter in the responses and they are not going to change anything in the interpretation of the data.

Head shape and fiducial points

*3D digitization using a Polhemus Fastrak device driven by Brainstorm (S01_20131218_*.pos) * More information: Digitize EEG electrodes and head shape_ * The output file is copied to each .ds folder and contains the following entries:

  • The position of the center of CTF coils

  • The position of the anatomical references we use in Brainstorm: Nasion and connections tragus/helix, as illustrated here_.

  • Around 150 head points distributed on the hard parts of the head (no soft tissues)

Subject anatomy

  • Subject with 1.5T MRI

  • Processed with FreeSurfer 5.3

Dataset Information#

Dataset ID

DS003082

Title

Auditory Cortex Mapping Dataset

Year

2020

Authors

Jonathan Cote, Etienne de Villers-Sidani

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds003082.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003082,
  title = {Auditory Cortex Mapping Dataset},
  author = {Jonathan Cote and Etienne de Villers-Sidani},
  doi = {10.18112/openneuro.ds003082.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003082.v1.0.1},
}

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: 1

  • Recordings: 62

  • Tasks: 2

Channels & sampling rate

  • Channels: 300 (2), 306

  • Sampling rate (Hz): 12000.0 (2), 2400.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 13.2 GB

  • File count: 62

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003082.v1.0.1

Provenance

API Reference#

Use the DS003082 class to access this dataset programmatically.

class eegdash.dataset.DS003082(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003082. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 2; recordings: 3; tasks: 2.

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/ds003082 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003082

Examples

>>> from eegdash.dataset import DS003082
>>> dataset = DS003082(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

See Also#