NM000485: ieeg dataset, 37 subjects#
s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative)
Access recordings and metadata through EEGDash.
Citation: Aliette Lochy, Corentin Jacques, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion, Jacques Jonas (2018). s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative). 10.82901/nemar.nm000485
Modality: ieeg Subjects: 37 Recordings: 111 License: CC0-1.0 Source: nemar
Metadata: Complete (100%)
37-participant iEEG dataset — s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000485
dataset = NM000485(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000485(cache_dir="./data", subject="01")
Advanced query
dataset = NM000485(
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{nm000485,
title = {s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative)},
author = {Aliette Lochy and Corentin Jacques and Louis Maillard and Sophie Colnat-Coulbois and Bruno Rossion and Jacques Jonas},
doi = {10.82901/nemar.nm000485},
url = {https://doi.org/10.82901/nemar.nm000485},
}
About This Dataset#
SEEG from 37 patients with refractory epilepsy (University Hospital of Nancy) viewing fast periodic visual stimulation:
pseudofonts, nonwords (non-fonts) or pseudowords at 10 Hz with a real word every fifth item (2 Hz). Selective responses to letters and words are measured at 2 Hz and its harmonics (article). THIS IS A DERIVATIVE DATASET: the release contains the authors’ Letswave files, low-pass filtered at 40 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-wordspseudofont: VC-W: words at 2 Hz among pseudofonts at 10 Hz -task-wordsnonfont: NW-W: words at 2 Hz among nonwords (non-fonts) at 10 Hz -task-wordspseudoword: PW-W: words at 2 Hz among pseudowords at 10 Hz
s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (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 participant’s folder has an xlsx table (Channel, Tal_x/y/z, MNI_x/y/z). They are written to
space-Talairach_electrodes.tsv(37 participants) andspace-Other_electrodes.tsv(MNI, 37 participants; the MNI template version is not stated in the release so the system is recorded as Other, see coordsystem.json). 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 (0 files have no Letswave events and no events.tsv); most frequent codes: [(‘s1’, 501795), (‘seq_stimuli_to_use_sinstim_Localiser_10Hz_VisualcontrolWords_Per_1’, 10), (‘seq_stimuli_to_use_sinstim_Localiser_10Hz_NonwordsWords_Per_1’, 6), (‘seq_stimuli_to_use_sinstim_Localiser_10Hz_PseudowordsWords_Per_1’, 6), (‘1’, 5), (‘unk’, 2), (‘NW_W’, 2), (‘seq_PW-W’, 1)].Letswave history per file is in each
ieeg.json(SoftwareFilters).sourcedata/dryad-6s39h64-deidentified/: the release’s files, de-identified (below);DEIDENTIFICATION_MANIFEST.tsvlists original and new sha-256 per file.stimuli/: the stimulus images (BMP) andstimuli_bigram_frequency.txtfrom the first zip’s Stimuli folder (released by the authors with the data).
Privacy
Letswave event codes with a leading participant-code token were replaced by ‘seq’ in events.tsv and in the de-identified .lw5 headers (26 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.nm000485) # s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative) SEEG from 37 patients with refractory epilepsy (University Hospital of Nancy) viewing fast periodic visual stimulation: pseudofonts, nonwords (non-fonts) or pseudowords at 10 Hz with a real word every fifth item (2 Hz). Selective responses to letters and words are measured at 2 Hz and its harmonics (article). THIS IS A DERIVATIVE DATASET: the release contains the authors’ Letswave files, low-pass filtered at 40 Hz (Butterworth order 4) and cut into sequences, not the raw Micromed recordings. ## Source - Dryad: Aliette Lochy, Corentin Jacques, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion, Jacques Jonas. Data from: Selective visual representation of letters and words in the left ventral occipito-temporal cortex with intracerebral recordings. doi:10.5061/dryad.6s39h64 (version 1, 2019-07-09). License: CC0 1.0 (Dryad record). - Article: Lochy A, Jacques C, Maillard L, Colnat-Coulbois S, Rossion B, Jonas J (2018) Selective visual representation of letters and words in the left ventral occipito-temporal cortex with intracerebral recordings. PNAS 115(32):E7595-E7604, doi:10.1073/pnas.1718987115. - Dryad files: 3 zips (participants P001-P012, P013-P024, P025-P037) + 3 READMEs, verified against the Dryad md5 digests by the download lane. ## Contents - sub-<id>/ieeg/sub-<id>_task-<task>_run-1_ieeg.*: 111 files, 37 participants, 6.11 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-wordspseudofont: VC-W: words at 2 Hz among pseudofonts at 10 Hz - task-wordsnonfont: NW-W: words at 2 Hz among nonwords (non-fonts) at 10 Hz - task-wordspseudoword: PW-W: words at 2 Hz among pseudowords at 10 Hz
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 participant’s folder has an xlsx table (Channel, Tal_x/y/z, MNI_x/y/z). They are written to space-Talairach_electrodes.tsv (37 participants) and space-Other_electrodes.tsv (MNI, 37 participants; the MNI template version is not stated in the release so the system is recorded as Other, see coordsystem.json). 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 (0 files have no Letswave events and no events.tsv); most frequent codes: [(‘s1’, 501795), (‘seq_stimuli_to_use_sinstim_Localiser_10Hz_VisualcontrolWords_Per_1’, 10), (‘seq_stimuli_to_use_sinstim_Localiser_10Hz_NonwordsWords_Per_1’, 6), (‘seq_stimuli_to_use_sinstim_Localiser_10Hz_PseudowordsWords_Per_1’, 6), (‘1’, 5), (‘unk’, 2), (‘NW_W’, 2), (‘seq_PW-W’, 1)].
Letswave history per file is in each ieeg.json (SoftwareFilters).
sourcedata/dryad-6s39h64-deidentified/: the release’s files, de-identified (below); DEIDENTIFICATION_MANIFEST.tsv lists original and new sha-256 per file.
stimuli/: the stimulus images (BMP) and stimuli_bigram_frequency.txt from the first zip’s Stimuli folder (released by the authors with the data).
## Privacy - Letswave event codes with a leading participant-code token were replaced by ‘seq’ in events.tsv and in the
de-identified .lw5 headers (26 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:
Aliette Lochy
Corentin Jacques
Louis Maillard
Sophie Colnat-Coulbois
Bruno Rossion
… and 1 more
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies: 512.0 Hz (n=111 recordings)
Total recording duration: 6 h 6 min
Signal · Electrodes & live trace#
Live trace viewer — sub-P021 · task-wordspseudofont · run-1
Showing one representative recording out of
37 subjects and 111 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 · 203 sensors — 203 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-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative) |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2018 |
Authors |
Aliette Lochy, Corentin Jacques, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion, Jacques Jonas |
License |
CC0-1.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000485,
title = {s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative)},
author = {Aliette Lochy and Corentin Jacques and Louis Maillard and Sophie Colnat-Coulbois and Bruno Rossion and Jacques Jonas},
doi = {10.82901/nemar.nm000485},
url = {https://doi.org/10.82901/nemar.nm000485},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000485(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative)
- Study:
nm000485(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000485.Modality:
ieeg; Subject type:Unknown. Subjects: 37; recordings: 111; 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/nm000485 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000485 DOI: https://doi.org/10.82901/nemar.nm000485
Examples
>>> from eegdash.dataset import NM000485 >>> dataset = NM000485(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 nm000485 to reproduce the tutorial on this dataset.
Citation
Aliette Lochy, Corentin Jacques, Louis Maillard, Sophie Colnat-Coulbois, Bruno Rossion, … (2018). s500-generic-45: Letter and word selective SEEG responses in the ventral occipito-temporal cortex, fast periodic visual stimulation (Lochy et al., 2018) (derivative). 10.82901/nemar.nm000485
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000485.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset