DS003645#

Face processing MEEG dataset with HED annotation

Access recordings and metadata through EEGDash.

Citation: Daniel G. Wakeman, Richard N Henson, Dung Truong (curation), Kay Robbins (curation), Scott Makeig (curation), Arno Delorme (curation) (2021). Face processing MEEG dataset with HED annotation. 10.18112/openneuro.ds003645.v2.0.2

Modality: eeg Subjects: 18 Recordings: 1137 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003645

dataset = DS003645(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 = DS003645(cache_dir="./data", subject="01")

Advanced query

dataset = DS003645(
    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{ds003645,
  title = {Face processing MEEG dataset with HED annotation},
  author = {Daniel G. Wakeman and Richard N Henson and Dung Truong (curation) and Kay Robbins (curation) and Scott Makeig (curation) and Arno Delorme (curation)},
  doi = {10.18112/openneuro.ds003645.v2.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003645.v2.0.2},
}

About This Dataset#

Introduction: This dataset consists of the MEEG (sMRI+MEG+EEG) portion of the multi-subject, multi-modal face processing dataset (ds000117). This dataset was originally acquired and shared by Daniel Wakeman and Richard Henson (https://pubmed.ncbi.nlm.nih.gov/25977808/). The MEG and EEG data were simultaneously recorded; the sMRI scans were preserved to support M/EEG source localization. Following event log augmentation, reorganization, and HED (v8.0.0) annotation, the EEG data have been repackaged in EEGLAB format.

Overview of the experiment: Eighteen participants completed two recording sessions spaced three months apart – one session recorded fMRI and the other simultaneously recorded MEG and EEG data. During each session, participants performed the same simple perceptual task, responding to presented photographs of famous, unfamiliar, and scrambled faces by pressing one of two keyboard keys to indicate a subjective yes or no decision as to the relative spatial symmetry of the viewed face. Famous faces were feature-matched to unfamiliar faces; half the faces were female. The two sessions (MEEG, fMRI) had different organizations of event timing and presentation because of technological requirements of the respective imaging modalities. Each individual face was presented twice during the session. For half of the presented faces, the second presentation followed immediately after the first. For the other half, the second presentation was delayed by 5-15 face presentations.

Preprocessing: The EEG preprocessing, which was performed using the wh_extracteeg_BIDS.m located in the code directory, includes the following steps: * Ignore MRI data except for sMRI. * Extract EEG channels out of the MEG/EEG fif data * Add fiducials * Rename EOG and EKG channels * Extract events from event channel * Add button press events! * Remove spurious event types 5, 6, 7, 13, 14, 15, 17, 18 and 19 * Remove spurious event types 24 for subject 3 run 4 * Correct event latencies (events have a shift of 34 ms) * Add HED (v8.0.0) event annotations – see Robbins et al. (2021) * Remove event fields urevent and duration * Save as EEGLAB .set format

Data curators: Dung Truong, Ramon Martinez, Scott Makeig, Arnaud Delorme (UCSD, La Jolla, CA, USA), Kay Robbins (UTSA, San Antonio, TX, USA)

Dataset Information#

Dataset ID	`DS003645`
Title	Face processing MEEG dataset with HED annotation
Year	2021
Authors	Daniel G. Wakeman, Richard N Henson, Dung Truong (curation), Kay Robbins (curation), Scott Makeig (curation), Arno Delorme (curation)
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003645.v2.0.2
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: 18
Recordings: 1137
Tasks: 1

Channels & sampling rate

Channels: 404 (120), 394 (96)
Sampling rate (Hz): 1100.0
Duration (hours): 0.0

API Reference#

Use the DS003645 class to access this dataset programmatically.

class eegdash.dataset.DS003645(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003645. Modality: eeg, meg; Experiment type: Perception; Subject type: Healthy. Subjects: 19; recordings: 224; 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/ds003645 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003645

Examples

>>> from eegdash.dataset import DS003645
>>> dataset = DS003645(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