EEGdash›NeMAR›ON000117

Iss. 117 · 17 subjects · 104 recordings · CC0

Dataset Brief · Multisubject, multimodal face processing

ON000117: meg dataset, 17 subjects#

Multisubject, multimodal face processing

Citation: Wakeman, DG, Henson, RN (2015). Multisubject, multimodal face processing. 10.82901/nemar.on000117

17-participant MEG dataset — Multisubject, multimodal face processing.

Data & curation Wakeman, DG · Henson, RN
Year 2015 · Distributed via NeMAR
Funding UK Medical Research Council (SUAG/010 RG91365), Elekta Ltd.

MEG · 394 ch1100 HzBIDS 1.0.22 tasks9 sessions

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import ON000117

dataset = ON000117(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 = ON000117(cache_dir="./data", subject="01")

Advanced query

dataset = ON000117(
    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{on000117,
  title = {Multisubject, multimodal face processing},
  author = {Wakeman, DG and Henson, RN},
  doi = {10.82901/nemar.on000117},
  url = {https://doi.org/10.82901/nemar.on000117},
}

§ 02Study · The README

About This Dataset#

This dataset was obtained from the OpenNeuro project (https://www.openneuro.org). Accession #: ds000117

The same dataset is also available here: ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/, but in a non-BIDS format (which may be easier to download by subject rather than by modality) Note that it is a subset of the data available on OpenfMRI (http://www.openfmri.org; Accession #: ds000117).

Description: Multi-subject, multi-modal (sMRI+fMRI+MEG+EEG) neuroimaging dataset on face processing

Please cite the following reference if you use these data:

Wakeman, D.G. & Henson, R.N. (2015). A multi-subject, multi-modal human neuroimaging dataset. Sci. Data 2:150001 doi: 10.1038/sdata.2015.1

DOI

The data have been used in several publications including, for example:

Henson, R.N., Abdulrahman, H., Flandin, G. & Litvak, V. (2019). Multimodal integration of M/EEG and f/MRI data in SPM12. Frontiers in Neuroscience, Methods, 13, 300.: Henson, R.N., Wakeman, D.G., Litvak, V. & Friston, K.J. (2011). A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration. Frontiers in Human Neuroscience, 5, 76, 1-16. Chapter 42 of the SPM12 manual (http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf)

(see ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/Publications for full list), as well as the BioMag2010 data competition and the Kaggle competition: https://www.kaggle.com/c/decoding-the-human-brain)

func/

View full README

DOI

The data have been used in several publications including, for example:

Henson, R.N., Abdulrahman, H., Flandin, G. & Litvak, V. (2019). Multimodal integration of M/EEG and f/MRI data in SPM12. Frontiers in Neuroscience, Methods, 13, 300.: Henson, R.N., Wakeman, D.G., Litvak, V. & Friston, K.J. (2011). A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration. Frontiers in Human Neuroscience, 5, 76, 1-16. Chapter 42 of the SPM12 manual (http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf)

(see ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/Publications for full list), as well as the BioMag2010 data competition and the Kaggle competition: https://www.kaggle.com/c/decoding-the-human-brain)

func/

Unlike in v1-v3 of this dataset, the first two (dummy) volumes have now been removed (as stated in *.json), so event onset times correctly refer to t=0 at start of third volume Note that, owing to scanner error, Subject 10 only has 170 volumes in last run (Run 9)

meg/

Three anatomical fiducials were digitized for aligning the MEG with the MRI: the nasion (lowest depression between the eyes) and the left and right ears (lowest depression between the tragus and the helix, above the tragus). This procedure is illustrated here: http://neuroimage.usc.edu/brainstorm/CoordinateSystems#Subject_Coordinate_System_.28SCS_.2F_CTF.29 and in task-facerecognition_fidinfo.pdf The following triggers are included in the .fif files and are also used in the “trigger” column of the meg and bold events files:

Trigger Label Simplified Label 5 Initial Famous Face IniFF 6 Immediate Repeat Famous Face ImmFF 7 Delayed Repeat Famous Face DelFF 13 Initial Unfamiliar Face IniUF 14 Immediate Repeat Unfamiliar Face ImmUF 15 Delayed Repeat Unfamiliar Face DelUF 17 Initial Scrambled Face IniSF 18 Immediate Repeat Scrambled Face ImmSF 19 Delayed Repeat Scrambled Face DelSF

stimuli/meg/

The .bmp files correspond to those described in the text. There are 6 additional images in this directory, which were used in the practice experiment to familiarize participants with the task (hence some more BIDS validator warnings)

stimuli/mri/

The .bmp files correspond to those described in the text.

Defacing

Defacing of MPRAGE T1 images was performed by the submitter. A subset of subjects have given consent for non-defaced versions to be shared - in which case, please contact rik.henson@mrc-cbu.cam.ac.uk.

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Known Issues

N/A

Relationship of Subject Numbering relative to other versions of Dataset

There are multiple versions of the dataset available on the web (see notes above), and these entailed a renumbering of the subjects for various reasons. Here are all the versions and how to match subjects between them (plus some rationale and history for different versions): 1. Original Paper (N=19): Wakeman & Henson (2015): doi:10.1038/sdata.2015.1

Number refers to order that tested (and some, eg 4, 7, 13 etc were excluded for not completing both MRI and MEG sessions)

openfMRI, renumbered from paper: http://openfmri.org/s3-browser/?prefix=ds000117/ds000117_R0.1.1/uncompressed/
Numbers 1-19 just made contiguous
FTP subset of N=16: ftp: ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/
This set was used for SPM Courses Designed to illustrate multimodal integration, so wanted good MRI+MEG+EEG data for all subjects Removed original subject_01 and subject_06 because bad EEG data; subject_19 because poor EEG and fMRI data (And renumbered subject_14 for some reason).
Current OpenNeuro subset N=16 used for (BIDS): https://openneuro.org/datasets/ds000117
OpenNeuro was rebranding of openfMRI, and enforced BIDS format Since this version designed to illustrate multi-modal BIDS, kept same numbering as FTP

W&H2015 openfMRI FTP openNeuro ======== ====== === ======= subject_01 sub001 subject_02 sub002 Sub01 sub-01 subject_03 sub003 Sub02 sub-02 subject_05 sub004 Sub03 sub-03 subject_06 sub005 subject_08 sub006 Sub05 sub-05 subject_09 sub007 Sub06 sub-06 subject_10 sub008 Sub07 sub-07 subject_11 sub009 Sub08 sub-08 subject_12 sub010 Sub09 sub-09 subject_14 sub011 Sub04 sub-04 subject_15 sub012 Sub10 sub-10 subject_16 sub013 Sub11 sub-11 subject_17 sub014 Sub12 sub-12 subject_18 sub015 Sub13 sub-13 subject_19 sub016 subject_23 sub017 Sub14 sub-14 subject_24 sub018 Sub15 sub-15 subject_25 sub019 Sub16 sub-16

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=16, range 23–31 yr, mean 26.4 yr)

202530

Female · 7Male · 9

Sex composition

Female
7
Male
9
F : M ratio
0.78 : 1

44% female · n = 16 subjects with reported sex.

Channel counts: 394 ch (n=96 recordings)

Sampling frequencies: 1100.0 Hz (n=96 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 394 ch · MEG · 1100 Hz · 17 subjects, 104 recordings

Live trace viewer — sub-01 · ses-meg · task-facerecognition · run-01

Showing one representative recording out of 17 subjects and 104 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _meg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?meg=<url>) to inspect it.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.ON000117(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON000117

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.ON000117(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Multisubject, multimodal face processing

Study:: on000117 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON000117, nan.

Modality: meg. Subjects: 17; recordings: 104; 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/on000117 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on000117 DOI: https://doi.org/10.82901/nemar.on000117

Examples

>>> from eegdash.dataset import ON000117
>>> dataset = ON000117(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — ON000117.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 on000117 to reproduce the tutorial on this dataset.

Citation

Wakeman, DG, Henson, RN (2015). Multisubject, multimodal face processing. 10.82901/nemar.on000117

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.on000117.

Related & sibling datasets

ON005261MEG · 17 subj DS007663MEG · 27 subj DS007640MEG · 23 subj DS004012MEG · 30 subj DS003694MEG · 28 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.2

Sidecars

events · coordsystem

Provenance

CC0 · 10.82901/nemar.on000117

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON000117`
Title	Multisubject, multimodal face processing
Author (year)	—
Canonical	—
Importable as	`ON000117`
Year	2015
Authors	Wakeman, DG, Henson, RN
License	CC0
Citation / DOI	10.82901/nemar.on000117
Source links	OpenNeuro \| NeMAR \| Source URL

ON000117: meg dataset, 17 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#