EEGdash›NeMAR›NM000537

Iss. 537 · 11 subjects · 46 recordings · GPL-3.0

Dataset Brief · Mapping effective connectivity of human amygdala with intracr…

NM000537: ieeg dataset, 11 subjects#

Name: Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials
License: GPL-3.0

Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials

Access recordings and metadata through EEGDash.

Citation: Hiroyuki Oya, LeiQi (20). Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials. 10.82901/nemar.nm000537

Modality: ieeg Subjects: 11 Recordings: 46 License: GPL-3.0 Source: nemar

Metadata: Complete (100%)

11-participant iEEG dataset — Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials.

Data & curation Hiroyuki Oya · LeiQi
Senior author LeiQi
Year 20 · Distributed via NeMAR

iEEG · 256 (16), 192 (14), 255 (12), 191 (2), 202, 254 ch8000 Hz · mixedBIDS 1.10.0Task · amygdalastim

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 NM000537

dataset = NM000537(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 = NM000537(cache_dir="./data", subject="01")

Advanced query

dataset = NM000537(
    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{nm000537,
  title = {Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials},
  author = {Hiroyuki Oya and LeiQi},
  doi = {10.82901/nemar.nm000537},
  url = {https://doi.org/10.82901/nemar.nm000537},
}

§ 02Study · The README

About This Dataset#

Evoked-potential epochs recorded with intracranial electrodes during single-pulse electrical stimulation of the amygdala

(“electrical stimulation tract-tracing”, es-TT) in neurosurgical patients, released on OSF by Hiroyuki Oya, LeiQi (doi:10.17605/OSF.IO/DNGV5; licence on the OSF node: GNU General Public License (GPL) 3.0) for Sawada et al., Nature Communications 2022 (doi:10.1038/s41467-022-32644-y). OSF description: “esTT data converted in to MATLAB formatted matrix.”

run folder>``), each the release’s EP_data3D matrix (typically 60 epochs) written back to back (``RecordingType\` epoched).

DOI

Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials (OSF dngv5)

Sampling rate: not stored in the release files. 8000 Hz (the article’s rate) for 39 of the 46 runs, 2000 Hz for sub-369 runs 91 and 92 and 16000 Hz for sub-515 (all 5 runs). Basis: in the pre-stimulus baseline, the 60 Hz mains harmonics (60/180/300/420 Hz) line up only under that rate (spectral check of 16 Oct 2026); for sub-515 the deposit’s 515_experiment_list.ods states FS 16k, filters 0.1-2000 Hz. sub-400 (runs 72, 73) keeps the article’s 8000 Hz, but its rate is unresolved: mains harmonics suggest 16 kHz, filter edge and artefact width suggest 8 kHz (checks of 17 Oct 2026). Correction (1.0.1, 2026-10-16): version 1.0.0 gave 8000 Hz for all runs. The BrainVision headers (_ieeg.vhdr) of the

View full README

DOI

Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials (OSF dngv5)

Sampling rate: not stored in the release files. 8000 Hz (the article’s rate) for 39 of the 46 runs, 2000 Hz for sub-369 runs 91 and 92 and 16000 Hz for sub-515 (all 5 runs). Basis: in the pre-stimulus baseline, the 60 Hz mains harmonics (60/180/300/420 Hz) line up only under that rate (spectral check of 16 Oct 2026); for sub-515 the deposit’s 515_experiment_list.ods states FS 16k, filters 0.1-2000 Hz. sub-400 (runs 72, 73) keeps the article’s 8000 Hz, but its rate is unresolved: mains harmonics suggest 16 kHz, filter edge and artefact width suggest 8 kHz (checks of 17 Oct 2026). Correction (1.0.1, 2026-10-16): version 1.0.0 gave 8000 Hz for all runs. The BrainVision headers (_ieeg.vhdr) of the 7 corrected runs (sub-369 runs 91/92, sub-515) still say 8000 Hz (SamplingInterval 125 us) until they are re-generated: use SamplingFrequency in _ieeg.json.
Epochs: each run is the release’s EP_data3D matrix (25-62 epochs per run: 60 in 37 runs, 30 in the 5 sub-515 runs) written back to back. Epoch length is 4001 samples in the 3 sub-369 runs (2.0005 s at 2 kHz for runs 91/92, 0.500125 s at 8 kHz for run 100), 32001 samples (2.0000625 s at 16 kHz) in the 5 sub-515 runs and 16001 samples in the other 38 runs (2.000125 s at 8 kHz). The stimulus artefact (sample index per run in code/conversion_report.json, usually the centre) is the epoch time zero and has an event in events.tsv. The stimulated amygdala contact pair is not identified in the release, except for sub-515 (stimulation channels per block in 515_experiment_list.ods, copied into ElectricalStimulationParameters).
Channels: only the release’s channel slots that contain data, named ch<slot>; the contact label from the patient’s spreadsheet (PT<n>.xlsx / Eleclocation.xlsx) is in the channel description. Typing rule actually applied: labels containing “depth” are SEEG, labels containing “strip” or “grid” are ECOG, and every other channel is also typed SEEG although its electrode type is not stated: channels with only an anatomical label (180-181 per run in sub-399 and sub-400, 11 in sub-376; e.g. “MTG”, “Orbital g.”) and channels with no spreadsheet label (all 255 of sub-515, 5-75 in each other subject). Units: microvolts (not stated in the release; baseline amplitudes of a few to a few hundred uV and clipping at about +/-1000 in many channels, counts per run in the conversion report; version 1.0.0 had n/a). Reference: subgaleal contact (article). Channels with NaN samples are marked bad.
sourcedata/osf_dngv5/: per-patient spreadsheets, Coordinates.mat (space not stated, so not used for electrodes.tsv) and the few EPdata2.mat files whose layout ([254 x 16002]) is undocumented; MAT text-header dates set to day 01.
Not included: es-fMRI folders (the fMRI data are on OpenNeuro ds002799) and the Granger-causality source data.

Provenance

Converted by the ieeg-nemar-20261005 campaign (convert_20261014, slice 6) with code/cv6_conv_lit165.py.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000537-blue)](https://doi.org/10.82901/nemar.nm000537) # Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials (OSF dngv5) Evoked-potential epochs recorded with intracranial electrodes during single-pulse electrical stimulation of the amygdala (“electrical stimulation tract-tracing”, es-TT) in neurosurgical patients, released on OSF by Hiroyuki Oya, LeiQi (doi:10.17605/OSF.IO/DNGV5; licence on the OSF node: GNU General Public License (GPL) 3.0) for Sawada et al., Nature Communications 2022 (doi:10.1038/s41467-022-32644-y). OSF description: “esTT data converted in to MATLAB formatted matrix.” ## Content - 11 participants (sub-<PT number>, the numbers used in the article), 46 stimulation runs (`run-<release

run folder>`), each the release’s EP_data3D matrix (typically 60 epochs) written back to back (RecordingType epoched).

Sampling rate: not stored in the release files. 8000 Hz (the article’s rate) for 39 of the 46 runs, 2000 Hz for sub-369 runs 91 and 92 and 16000 Hz for sub-515 (all 5 runs). Basis: in the pre-stimulus baseline, the 60 Hz mains harmonics (60/180/300/420 Hz) line up only under that rate (spectral check of 16 Oct 2026); for sub-515 the deposit’s 515_experiment_list.ods states FS 16k, filters 0.1-2000 Hz. sub-400 (runs 72, 73) keeps the article’s 8000 Hz, but its rate is unresolved: mains harmonics suggest 16 kHz, filter edge and artefact width suggest 8 kHz (checks of 17 Oct 2026). Correction (1.0.1, 2026-10-16): version 1.0.0 gave 8000 Hz for all runs. The BrainVision headers (_ieeg.vhdr) of the 7 corrected runs (sub-369 runs 91/92, sub-515) still say 8000 Hz (SamplingInterval 125 us) until they are re-generated: use SamplingFrequency in _ieeg.json.
Epochs: each run is the release’s EP_data3D matrix (25-62 epochs per run: 60 in 37 runs, 30 in the 5 sub-515 runs) written back to back. Epoch length is 4001 samples in the 3 sub-369 runs (2.0005 s at 2 kHz for runs 91/92, 0.500125 s at 8 kHz for run 100), 32001 samples (2.0000625 s at 16 kHz) in the 5 sub-515 runs and 16001 samples in the other 38 runs (2.000125 s at 8 kHz). The stimulus artefact (sample index per run in code/conversion_report.json, usually the centre) is the epoch time zero and has an event in events.tsv. The stimulated amygdala contact pair is not identified in the release, except for sub-515 (stimulation channels per block in 515_experiment_list.ods, copied into ElectricalStimulationParameters).
Channels: only the release’s channel slots that contain data, named ch<slot>; the contact label from the patient’s spreadsheet (PT<n>.xlsx / Eleclocation.xlsx) is in the channel description. Typing rule actually applied: labels containing “depth” are SEEG, labels containing “strip” or “grid” are ECOG, and every other channel is also typed SEEG although its electrode type is not stated: channels with only an anatomical label (180-181 per run in sub-399 and sub-400, 11 in sub-376; e.g. “MTG”, “Orbital g.”) and channels with no spreadsheet label (all 255 of sub-515, 5-75 in each other subject). Units: microvolts (not stated in the release; baseline amplitudes of a few to a few hundred uV and clipping at about +/-1000 in many channels, counts per run in the conversion report; version 1.0.0 had n/a). Reference: subgaleal contact (article). Channels with NaN samples are marked bad.
sourcedata/osf_dngv5/: per-patient spreadsheets, Coordinates.mat (space not stated, so not used for electrodes.tsv) and the few EPdata2.mat files whose layout ([254 x 16002]) is undocumented; MAT text-header dates set to day 01.
Not included: es-fMRI folders (the fMRI data are on OpenNeuro ds002799) and the Granger-causality source data.

## Provenance Converted by the ieeg-nemar-20261005 campaign (convert_20261014, slice 6) with code/cv6_conv_lit165.py.

License: GPL-3.0

Authors:

Hiroyuki Oya
LeiQi

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000537

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

191192202254255256

Sampling frequencies (Hz)

2000800016000

Total recording duration: 1 h 22 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 256 (16), 192 (14), 255 (12), 191 (2), 202, 254 ch · iEEG · 8000 Hz · mixed · 11 subjects, 46 recordings

Live trace viewer — sub-369 · task-amygdalastim · run-91

Showing one representative recording out of 11 subjects and 46 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.

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.NM000537(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000537

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

class eegdash.dataset.NM000537(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials

Study:: nm000537 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000537.

Modality: ieeg; Subject type: Unknown. Subjects: 11; recordings: 46; 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/nm000537 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000537 DOI: https://doi.org/10.82901/nemar.nm000537

Examples

>>> from eegdash.dataset import NM000537
>>> dataset = NM000537(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 — NM000537.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 nm000537 to reproduce the tutorial on this dataset.

Citation

Hiroyuki Oya, LeiQi (20). Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials. 10.82901/nemar.nm000537

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000537.

Related & sibling datasets

NM000484IEEG · 1 subj NM000435IEEG · 11 subj NM000443IEEG · 7 subj NM000463IEEG · 2 subj NM000455IEEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

GPL-3.0 · 10.82901/nemar.nm000537

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000537`
Title	Mapping effective connectivity of human amygdala with intracranial stimulation - es-TT evoked potentials
Author (year)	—
Canonical	—
Importable as	`NM000537`
Year	20
Authors	Hiroyuki Oya, LeiQi
License	GPL-3.0
Citation / DOI	10.82901/nemar.nm000537
Source links	OpenNeuro \| NeMAR \| Source URL

NM000537: ieeg dataset, 11 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#