NM000475: ieeg dataset, 3 subjects#
s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative)
Access recordings and metadata through EEGDash.
Citation: Angélique Volfart, Jacques Jonas, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion (2020). s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative). 10.82901/nemar.nm000475
Modality: ieeg Subjects: 3 Recordings: 9 License: CC0-1.0 Source: nemar
Metadata: Complete (100%)
3-participant iEEG dataset — s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000475
dataset = NM000475(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000475(cache_dir="./data", subject="01")
Advanced query
dataset = NM000475(
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{nm000475,
title = {s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative)},
author = {Angélique Volfart and Jacques Jonas and Louis Maillard and Sophie Colnat-Coulbois and Bruno Rossion},
doi = {10.82901/nemar.nm000475},
url = {https://doi.org/10.82901/nemar.nm000475},
}
About This Dataset#
SEEG (Experiment 2 of the article) from 7 patients with refractory epilepsy (University Hospital of Nancy) during crossmodal
frequency tagging: pictures of faces and written names of a famous identity at 4 Hz with another identity every 7th item (0.571 Hz), plus face-only and name-only control conditions. The scalp-EEG experiments of the release (Experiment 1 and Experiment 2, Biosemi 128, trial-averaged Letswave files) are NOT converted; their de-identified files are in sourcedata/. NOTE: in the Dryad release (version 2) the SEEG .mat data files of participants [‘P01’, ‘P02’, ‘P04’, ‘P05’] are EMPTY MATLAB arrays (1960-byte files whose ‘data’ carries the MATLAB_empty flag; the .lw5 headers list the channels but datasize has 0 sequences). Only the 3 participants with data (P03, P06, P07) are converted; the empty files are kept in sourcedata/ as released. THIS IS A DERIVATIVE DATASET: the release contains the authors’ Letswave files, low-pass filtered at 30 Hz (Butterworth order 4) and cut into sequences, not the raw Micromed recordings.
sequences of one condition back-to-back (one BrainVision segment per sequence;
RecordingTypeepoched,EpochLength=sequence length). Values are the release’s float32 values, unchanged (MNE read-back matches). Sampling rate from the Letswave header. Tasks: -
task-facename: FaceName: faces and names of one identity at 4 Hz, identity change every 7th item (0.571 Hz) -task-faceonly: FaceOnly: faces only, identity change every 7th item -task-nameonly: NameOnly: names only, identity change every 7th item
s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative)
Channels: labels from the Letswave header. Intracerebral contacts are typed SEEG; scalp electrodes recorded with the SEEG (labels
s<10-20 name>) EEG; ECG, physiological monitors (MISC) and marker inputs (TRIG) as labelled. Unit µV is assumed (Letswave import of Micromed TRC; not stated in the release). The reference is not stated per participant.Electrode coordinates: each SEEG participant’s folder has an xlsx table of Talairach coordinates (README: ‘Talairach MNI space’, used for display only; anatomical definitions were made on individual anatomy). They are written to
space-Talairach_electrodes.tsv(3 participants). Contacts not present in the table have x/y/z n/a.events.tsv: the Letswave events (code, sequence, latency) of each file that has any (9 files have no Letswave events and no events.tsv); most frequent codes: [].Letswave history per file is in each
ieeg.json(SoftwareFilters).sourcedata/dryad-m8t391m-deidentified/: the release’s files, de-identified (below);DEIDENTIFICATION_MANIFEST.tsvlists original and new sha-256 per file.No stimuli are included in the release.
Privacy
Letswave event codes with a leading participant-code token were replaced by ‘seq’ in events.tsv and in the de-identified .lw5 headers (0 distinct tokens; a byte search of the whole output found 0 remaining occurrences). Letswave history dates and MAT text-header dates in sourcedata are reduced to month and year (day -> 01). The .lw5 headers were rewritten with scipy (savemat, MATLAB v5, compressed); their content is otherwise unchanged.
Age and sex are not given per participant in the release.
NEMAR Metadata#
[](https://doi.org/10.82901/nemar.nm000475) # s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative) SEEG (Experiment 2 of the article) from 7 patients with refractory epilepsy (University Hospital of Nancy) during crossmodal frequency tagging: pictures of faces and written names of a famous identity at 4 Hz with another identity every 7th item (0.571 Hz), plus face-only and name-only control conditions. The scalp-EEG experiments of the release (Experiment 1 and Experiment 2, Biosemi 128, trial-averaged Letswave files) are NOT converted; their de-identified files are in sourcedata/. NOTE: in the Dryad release (version 2) the SEEG .mat data files of participants [‘P01’, ‘P02’, ‘P04’, ‘P05’] are EMPTY MATLAB arrays (1960-byte files whose ‘data’ carries the MATLAB_empty flag; the .lw5 headers list the channels but datasize has 0 sequences). Only the 3 participants with data (P03, P06, P07) are converted; the empty files are kept in sourcedata/ as released. THIS IS A DERIVATIVE DATASET: the release contains the authors’ Letswave files, low-pass filtered at 30 Hz (Butterworth order 4) and cut into sequences, not the raw Micromed recordings. ## Source - Dryad: Angélique Volfart, Jacques Jonas, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion. Data from: Neurophysiological evidence for crossmodal person-identity representation in the human left ventral temporal cortex. doi:10.5061/dryad.m8t391m (version 2, 2020-02-28). License: CC0 1.0 (Dryad record). - Article: Volfart A, Jonas J, Maillard L, Colnat-Coulbois S, Rossion B (2020) Neurophysiological evidence for crossmodal (face-name) person-identity representation in the human left ventral temporal cortex. PLoS Biology 18(4):e3000659, doi:10.1371/journal.pbio.3000659. - Dryad files: FaceNameAssociation_Metadata.zip + README, verified against the Dryad md5 digests by the download lane. ## Contents - sub-<id>/ieeg/sub-<id>_task-<task>_run-1_ieeg.*: 9 files, 3 participants, 0.32 h. Each file holds the
sequences of one condition back-to-back (one BrainVision segment per sequence; RecordingType epoched, EpochLength = sequence length). Values are the release’s float32 values, unchanged (MNE read-back matches). Sampling rate from the Letswave header. Tasks: - task-facename: FaceName: faces and names of one identity at 4 Hz, identity change every 7th item (0.571 Hz) - task-faceonly: FaceOnly: faces only, identity change every 7th item - task-nameonly: NameOnly: names only, identity change every 7th item
Channels: labels from the Letswave header. Intracerebral contacts are typed SEEG; scalp electrodes recorded with the SEEG (labels s<10-20 name>) EEG; ECG, physiological monitors (MISC) and marker inputs (TRIG) as labelled. Unit µV is assumed (Letswave import of Micromed TRC; not stated in the release). The reference is not stated per participant.
Electrode coordinates: each SEEG participant’s folder has an xlsx table of Talairach coordinates (README: ‘Talairach MNI space’, used for display only; anatomical definitions were made on individual anatomy). They are written to space-Talairach_electrodes.tsv (3 participants). Contacts not present in the table have x/y/z n/a.
events.tsv: the Letswave events (code, sequence, latency) of each file that has any (9 files have no Letswave events and no events.tsv); most frequent codes: [].
Letswave history per file is in each ieeg.json (SoftwareFilters).
sourcedata/dryad-m8t391m-deidentified/: the release’s files, de-identified (below); DEIDENTIFICATION_MANIFEST.tsv lists original and new sha-256 per file.
No stimuli are included in the release.
## Privacy - Letswave event codes with a leading participant-code token were replaced by ‘seq’ in events.tsv and in the
de-identified .lw5 headers (0 distinct tokens; a byte search of the whole output found 0 remaining occurrences). Letswave history dates and MAT text-header dates in sourcedata are reduced to month and year (day -> 01). The .lw5 headers were rewritten with scipy (savemat, MATLAB v5, compressed); their content is otherwise unchanged.
Age and sex are not given per participant in the release.
License: CC0-1.0
Authors:
Angélique Volfart
Jacques Jonas
Louis Maillard
Sophie Colnat-Coulbois
Bruno Rossion
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 19 min
Signal · Electrodes & live trace#
Live trace viewer — sub-P06 · task-faceonly · run-1
Showing one representative recording out of
3 subjects and 9 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _ieeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?ieeg=<url>) to inspect it.
Electrode layout — iEEG · 112 sensors — 112 channels
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
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.
Full dataset metadata table
Dataset ID |
|
Title |
s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative) |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2020 |
Authors |
Angélique Volfart, Jacques Jonas, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion |
License |
CC0-1.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000475,
title = {s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative)},
author = {Angélique Volfart and Jacques Jonas and Louis Maillard and Sophie Colnat-Coulbois and Bruno Rossion},
doi = {10.82901/nemar.nm000475},
url = {https://doi.org/10.82901/nemar.nm000475},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000475(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative)
- Study:
nm000475(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000475.Modality:
ieeg; Subject type:Unknown. Subjects: 3; recordings: 9; tasks: 3.- 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/nm000475 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000475 DOI: https://doi.org/10.82901/nemar.nm000475
Examples
>>> from eegdash.dataset import NM000475 >>> dataset = NM000475(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 nm000475 to reproduce the tutorial on this dataset.
Citation
Angélique Volfart, Jacques Jonas, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion (2020). s500-generic-44: Crossmodal face-name person-identity frequency tagging, SEEG of the left ventral temporal cortex (Volfart et al., 2020) (derivative). 10.82901/nemar.nm000475
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000475.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset