DS002718#

Face processing EEG dataset for EEGLAB

Access recordings and metadata through EEGDash.

Citation: Daniel G. Wakeman, Richard N Henson (2020). Face processing EEG dataset for EEGLAB. 10.18112/openneuro.ds002718.v1.1.0

Modality: eeg Subjects: 18 Recordings: 133 License: CC0 Source: openneuro Citations: 11.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002718

dataset = DS002718(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 = DS002718(cache_dir="./data", subject="01")

Advanced query

dataset = DS002718(
    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{ds002718,
  title = {Face processing EEG dataset for EEGLAB},
  author = {Daniel G. Wakeman and Richard N Henson},
  doi = {10.18112/openneuro.ds002718.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds002718.v1.1.0},
}

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: Multi-subject, multi-modal (sMRI+EEG) neuroimaging dataset on face processing. Original data described at https://www.nature.com/articles/sdata20151 This is repackaged version of the EEG data in EEGLAB format. The data has gone through

View full README

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: Multi-subject, multi-modal (sMRI+EEG) neuroimaging dataset on face processing. Original data described at https://www.nature.com/articles/sdata20151 This is repackaged version of the EEG data in EEGLAB format. The data has gone through minimal preprocessing including (see wh_extracteeg_BIDS.m): - Ignoring fMRI and MEG data (sMRI preserved for EEG source localization) - Extracting EEG channels out of the MEG/EEG fif data - Adding fiducials - Renaming EOG and EKG channels - Extracting events from event channel - Removing spurious events 5, 6, 7, 13, 14, 15, 17, 18 and 19 - Removing spurious event 24 for subject 3 run 4 - Renaming events taking into account button assigned to each subject - Correcting event latencies (events have a shift of 34 ms) - Resampling data to 250 Hz (this is a step that is done because

this dataset is used as tutorial for EEGLAB and need to be lightweight)

Merging run 1 to 6
Removing event fields urevent and duration
Filling up empty fields for events boundary and stim_file.
Saving as EEGLAB .set format

Original and related datasets This data is a mapping of the original openfmri dataset ds000117 on OpenfMRI, which is no longer available (although a copy is available in the sourcedata folder of the ds003645 repository). The ds000117 dataset on OpenNeuro contains only 16 subjects. The original OpenfMRI dataset is described at the bottom of this README file https://openneuro.org/datasets/ds000117/versions/1.0.4/file-display/README along with the correspondance with the 16 subjects in ds000117. Note that sub-001 data on OpenfMRI was corrupted so it is not included here.

The openneuro dataset ds003645 is similar to this one but also contains MEG data and HED events. Also, it does not have the different runs merged.

Import warning Make sure to import the channel locations from the BIDS electrodes.tsv files. The EEGLAB .set files also contain channel locations, although they differ for subjects 8 and 14 because the .set version is wrong and rotated by 90 degrees. When using the EEGLAB EEG BIDS plugin, the default behavior is to import channel locations from BIDS.

Data curators: Ramon Martinez, Dung Truong, Scott Makeig, Arnaud Delorme (UCSD, La Jolla, CA, USA)

Dataset Information#

Dataset ID	`DS002718`
Title	Face processing EEG dataset for EEGLAB
Year	2020
Authors	Daniel G. Wakeman, Richard N Henson
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds002718.v1.1.0
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: 18
Recordings: 133
Tasks: 1

Channels & sampling rate

Channels: 70 (36), 74 (18)
Sampling rate (Hz): 250.0
Duration (hours): 0.0

API Reference#

Use the DS002718 class to access this dataset programmatically.

class eegdash.dataset.DS002718(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002718. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 18; recordings: 18; 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/ds002718 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002718

Examples

>>> from eegdash.dataset import DS002718
>>> dataset = DS002718(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