NM000487: ieeg dataset, 140 subjects#
s500-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative)
Access recordings and metadata through EEGDash.
Citation: Corentin Jacques, Jacques Jonas, Sophie Colnat-Coulbois, Bruno Rossion (2025). s500-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative). 10.82901/nemar.nm000487
Modality: ieeg Subjects: 140 Recordings: 140 License: CC-BY-4.0 Source: nemar
Metadata: Complete (100%)
140-participant iEEG dataset — s500-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000487
dataset = NM000487(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000487(cache_dir="./data", subject="01")
Advanced query
dataset = NM000487(
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{nm000487,
title = {s500-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative)},
author = {Corentin Jacques and Jacques Jonas and Sophie Colnat-Coulbois and Bruno Rossion},
doi = {10.82901/nemar.nm000487},
url = {https://doi.org/10.82901/nemar.nm000487},
}
About This Dataset#
Intracerebral EEG (SEEG) from 140 patients with refractory epilepsy (University Hospital of Nancy) viewing fast periodic
visual stimulation: natural images of objects at 6 Hz with a face every 5th image (1.2 Hz). 140 files, 9.67 h of sequences, 22160 SEEG channels in total (20495 with Talairach coordinates). Sampling rate {512.0: 126, 256.0: 3, 500.0: 11} (files per rate).
Released by the authors on OSF (https://osf.io/2qzym/, CC-BY 4.0) with the Letswave loader and participant tables.
THIS IS A DERIVATIVE DATASET: the release holds the authors’ minimally preprocessed Letswave 5 files (segmented into stimulation sequences and merged per participant; no filtering stated), not the raw Micromed recordings.
Jacques, Jonas, Colnat-Coulbois & Rossion (2025): face-selective SEEG across the ventral occipito-temporal cortex (140 participants)
Contents
sub-NNN/ieeg/sub-NNN_task-fpvsfaces_run-1_ieeg.*: the participant’s FPVS sequences back-to-back (one BrainVision segment per sequence;RecordingTypeepoched,EpochLength= sequence length incl. the 2 s before and after). Values are the release’s float32 values, unchanged. The sequence time axis starts atxstart(-2 s, or -1.8333 s for participants whose first trigger was missing;participants.tsvfirst_trigger_missing,ieeg.jsonSequenceStartTime).
View full README
Jacques, Jonas, Colnat-Coulbois & Rossion (2025): face-selective SEEG across the ventral occipito-temporal cortex (140 participants)
Contents
sub-NNN/ieeg/sub-NNN_task-fpvsfaces_run-1_ieeg.*: the participant’s FPVS sequences back-to-back (one BrainVision segment per sequence;RecordingTypeepoched,EpochLength= sequence length incl. the 2 s before and after). Values are the release’s float32 values, unchanged. The sequence time axis starts atxstart(-2 s, or -1.8333 s for participants whose first trigger was missing;participants.tsvfirst_trigger_missing,ieeg.jsonSequenceStartTime).channels.tsv: labels from the Letswave header (a prime ‘ marks the left hemisphere); all contacts typed SEEG; unit µV assumed (Letswave import of Micromed TRC; not stated in the release); reference = the participant’s reference contact from140_participants_seeg_info.txt.sub-NNN_space-Talairach_electrodes.tsv: Talairach x/y/z per contact from the Letswave header (chanlocs.xyz_coord_Talairach) plus the authors’ anatomical labels:anatomy_main_region(OCC, PTL, ATL, MTL, TP, WM…),anatomy_sub_regionandvotc(inside the authors’ VOTC region or not). Contacts without finite coordinates have n/a.events.tsv: Letswave events (trigger codes, sequence, latency); the README says the codes are meaningless for most participants (stimulus onsets are periodic).participants.tsv: age at SEEG, sex, handedness (release demographics), reference contact, first-trigger flag, sequence length, sampling rate, number of sequences and of channels.sourcedata/osf-2qzym-deidentified/: the release’s Letswave files per participant (headers rewritten, see Privacy), the README, loader and the two participant .txt tables (140_participants_info.xlsxis not copied: it could not be inspected in the conversion environment; the .txt tables carry the same information);DEIDENTIFICATION_MANIFEST.tsvlists original and new sha-256 per file.
Privacy
Letswave event codes with a leading participant-code token were replaced (‘seq_…’; other free-text codes ‘unk’) in events.tsv and in the de-identified .lw5 headers (108 distinct tokens; a byte search of the output found 0 remaining occurrences). Header
namefields were blanked, history dates reduced to month/year and file paths removed; MAT text-header dates reduced to month/year.Participants are identified by number only; demographics are age, sex and handedness as released.
NEMAR Metadata#
[](https://doi.org/10.82901/nemar.nm000487) # Jacques, Jonas, Colnat-Coulbois & Rossion (2025): face-selective SEEG across the ventral occipito-temporal cortex (140 participants) Intracerebral EEG (SEEG) from 140 patients with refractory epilepsy (University Hospital of Nancy) viewing fast periodic visual stimulation: natural images of objects at 6 Hz with a face every 5th image (1.2 Hz). 140 files, 9.67 h of sequences, 22160 SEEG channels in total (20495 with Talairach coordinates). Sampling rate {512.0: 126, 256.0: 3, 500.0: 11} (files per rate). Released by the authors on OSF (https://osf.io/2qzym/, CC-BY 4.0) with the Letswave loader and participant tables. THIS IS A DERIVATIVE DATASET: the release holds the authors’ minimally preprocessed Letswave 5 files (segmented into stimulation sequences and merged per participant; no filtering stated), not the raw Micromed recordings. ## Contents - sub-NNN/ieeg/sub-NNN_task-fpvsfaces_run-1_ieeg.*: the participant’s FPVS sequences back-to-back (one BrainVision segment per
sequence; RecordingType epoched, EpochLength = sequence length incl. the 2 s before and after). Values are the release’s float32 values, unchanged. The sequence time axis starts at xstart (-2 s, or -1.8333 s for participants whose first trigger was missing; participants.tsv first_trigger_missing, ieeg.json SequenceStartTime).
channels.tsv: labels from the Letswave header (a prime ‘ marks the left hemisphere); all contacts typed SEEG; unit µV assumed (Letswave import of Micromed TRC; not stated in the release); reference = the participant’s reference contact from 140_participants_seeg_info.txt.
sub-NNN_space-Talairach_electrodes.tsv: Talairach x/y/z per contact from the Letswave header (chanlocs.xyz_coord_Talairach) plus the authors’ anatomical labels: anatomy_main_region (OCC, PTL, ATL, MTL, TP, WM…), anatomy_sub_region and votc (inside the authors’ VOTC region or not). Contacts without finite coordinates have n/a.
events.tsv: Letswave events (trigger codes, sequence, latency); the README says the codes are meaningless for most participants (stimulus onsets are periodic).
participants.tsv: age at SEEG, sex, handedness (release demographics), reference contact, first-trigger flag, sequence length, sampling rate, number of sequences and of channels.
sourcedata/osf-2qzym-deidentified/: the release’s Letswave files per participant (headers rewritten, see Privacy), the README, loader and the two participant .txt tables (140_participants_info.xlsx is not copied: it could not be inspected in the conversion environment; the .txt tables carry the same information); DEIDENTIFICATION_MANIFEST.tsv lists original and new sha-256 per file.
## Privacy - Letswave event codes with a leading participant-code token were replaced (’seq_…’; other free-text codes ‘unk’) in
events.tsv and in the de-identified .lw5 headers (108 distinct tokens; a byte search of the output found 0 remaining occurrences). Header name fields were blanked, history dates reduced to month/year and file paths removed; MAT text-header dates reduced to month/year.
Participants are identified by number only; demographics are age, sex and handedness as released.
License: CC-BY-4.0
Authors:
Corentin Jacques
Jacques Jonas
Sophie Colnat-Coulbois
Bruno Rossion
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Age distribution by gender (n=140, range 14–56 yr, mean 33.0 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 9 h 39 min
Signal · Electrodes & live trace#
Live trace viewer — sub-031 · task-fpvsfaces · run-1
Showing one representative recording out of
140 subjects and 140 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 · 164 sensors — 164 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-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative) |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2025 |
Authors |
Corentin Jacques, Jacques Jonas, Sophie Colnat-Coulbois, Bruno Rossion |
License |
CC-BY-4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000487,
title = {s500-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative)},
author = {Corentin Jacques and Jacques Jonas and Sophie Colnat-Coulbois and Bruno Rossion},
doi = {10.82901/nemar.nm000487},
url = {https://doi.org/10.82901/nemar.nm000487},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000487(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
s500-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative)
- Study:
nm000487(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000487.Modality:
ieeg; Subject type:Unknown. Subjects: 140; recordings: 140; 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
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/nm000487 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000487 DOI: https://doi.org/10.82901/nemar.nm000487
Examples
>>> from eegdash.dataset import NM000487 >>> dataset = NM000487(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 nm000487 to reproduce the tutorial on this dataset.
Citation
Corentin Jacques, Jacques Jonas, Sophie Colnat-Coulbois, Bruno Rossion (2025). s500-literature-164: Face-selective SEEG across the ventral occipito-temporal cortex in 140 participants, fast periodic visual stimulation (Jacques et al., 2025) (derivative). 10.82901/nemar.nm000487
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000487.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset