EEGdash›NeMAR›NM000469

Iss. 469 · 1 subjects · 2 recordings · CC-BY-NC-4.0

Dataset Brief · Xie2026 amygdala ablation PTSD

NM000469: ieeg dataset, 1 subjects#

Xie2026 amygdala ablation PTSD: epoched stereo-EEG of the index patient before and after right amygdala radiofrequency ablation (interpretation-bias task)

Access recordings and metadata through EEGDash.

Citation: Tao Xie, Sanne J. H. van Rooij, Sai Sun, Noah K. Bryson, Phillip Demarest, Haeorum Park, Luigi Maccotta, Shuo Wang, Peter Brunner, Jon T. Willie (20). Xie2026 amygdala ablation PTSD: epoched stereo-EEG of the index patient before and after right amygdala radiofrequency ablation (interpretation-bias task). 10.82901/nemar.nm000469

Modality: ieeg Subjects: 1 Recordings: 2 License: CC-BY-NC-4.0 Source: nemar

Metadata: Complete (100%)

1-participant iEEG dataset — Xie2026 amygdala ablation PTSD: epoched stereo-EEG of the index patient before and after right amygdala radiofrequency ablation (interpretation-bias task).

Data & curation Tao Xie · Sanne J. H. van Rooij · Sai Sun · Noah K. Bryson · Phillip Demarest · Haeorum Park · …
Senior author Jon T. Willie
Year 20 · Distributed via NeMAR
Funding NIH R01-MH120194 · NIH K01-MH121653 · + 6 more

iEEG · 157 ch200 HzBIDS 1.10.0Task · interpretation2 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 NM000469

dataset = NM000469(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 = NM000469(cache_dir="./data", subject="01")

Advanced query

dataset = NM000469(
    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{nm000469,
  title = {Xie2026 amygdala ablation PTSD: epoched stereo-EEG of the index patient before and after right amygdala radiofrequency ablation (interpretation-bias task)},
  author = {Tao Xie and Sanne J. H. van Rooij and Sai Sun and Noah K. Bryson and Phillip Demarest and Haeorum Park and Luigi Maccotta and Shuo Wang and Peter Brunner and Jon T. Willie},
  doi = {10.82901/nemar.nm000469},
  url = {https://doi.org/10.82901/nemar.nm000469},
}

§ 02Study · The README

About This Dataset#

Epoched stereo-EEG (157 contacts, 200 Hz, 588 trials, 0.21 h of epochs) from one patient with

post-traumatic stress disorder recorded during a face interpretation-bias task before (ses-pre) and after (ses-post) SEEG-guided radiofrequency ablation of the right amygdala. Released by the authors on OSF (https://osf.io/63tym/, CC-BY-NC-4.0) as indexPatient.mat (“de-identified SEEG signals, event markers, and channel information for the index patient”) with the analysis script analysis_script.m / IndexPatient_example_code.m (in sourcedata).

events.tsv gives each trial’s position, the authors’ trial fields and the trial’s index in the released array).

Epoch window -0.5 to 0.795 s (eeg.times), 260 samples at 200 Hz. Signal units are not stated (units = n/a; values are the released float32 numbers unchanged; the preprocessing is described in the article).

DOI

Xie et al. 2026: SEEG of the index PTSD patient before and after right amygdala ablation

electrodes.tsv (space-Other): coordinates from chanlocs.tala (field name suggests Talairach; template not stated in the release), with the released per-contact anatomical labels (annoLab, e.g. amygdala nuclei) and categories (annoCat).
participants.tsv: age and sex are not in the release.

Privacy

Release described as de-identified; string scan of the MAT file: 1 flagged strings. MAT header export date day set to 01 in sourcedata.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000469-blue)](https://doi.org/10.82901/nemar.nm000469) # Xie et al. 2026: SEEG of the index PTSD patient before and after right amygdala ablation Epoched stereo-EEG (157 contacts, 200 Hz, 588 trials, 0.21 h of epochs) from one patient with post-traumatic stress disorder recorded during a face interpretation-bias task before (ses-pre) and after (ses-post) SEEG-guided radiofrequency ablation of the right amygdala. Released by the authors on OSF (https://osf.io/63tym/, CC-BY-NC-4.0) as indexPatient.mat (“de-identified SEEG signals, event markers, and channel information for the index patient”) with the analysis script analysis_script.m / IndexPatient_example_code.m (in sourcedata). ## Organisation (DatasetType derivative) - One BrainVision file per session with the released epochs written back-to-back (one New Segment marker per trial;

events.tsv gives each trial’s position, the authors’ trial fields and the trial’s index in the released array). Epoch window -0.5 to 0.795 s (eeg.times), 260 samples at 200 Hz. Signal units are not stated (units = n/a; values are the released float32 numbers unchanged; the preprocessing is described in the article).

electrodes.tsv (space-Other): coordinates from chanlocs.tala (field name suggests Talairach; template not stated in the release), with the released per-contact anatomical labels (annoLab, e.g. amygdala nuclei) and categories (annoCat).
participants.tsv: age and sex are not in the release.

## Privacy Release described as de-identified; string scan of the MAT file: 1 flagged strings. MAT header export date day set to 01 in sourcedata.

License: CC-BY-NC-4.0

Authors:

Tao Xie
Sanne J. H. van Rooij
Sai Sun
Noah K. Bryson
Phillip Demarest
… and 5 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000469

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 157 ch (n=2 recordings)

Sampling frequencies: 200.0 Hz (n=2 recordings)

Total recording duration: 12 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 157 ch · iEEG · 200 Hz · 1 subjects, 2 recordings

Live trace viewer — sub-01 · ses-pre · task-interpretation

Showing one representative recording out of 1 subjects and 2 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 · 157 sensors — 157 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

§ 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.NM000469(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000469

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

class eegdash.dataset.NM000469(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Xie2026 amygdala ablation PTSD: epoched stereo-EEG of the index patient before and after right amygdala radiofrequency ablation (interpretation-bias task)

Study:: nm000469 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000469.

Modality: ieeg; Subject type: Unknown. Subjects: 1; recordings: 2; 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/nm000469 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000469 DOI: https://doi.org/10.82901/nemar.nm000469

Examples

>>> from eegdash.dataset import NM000469
>>> dataset = NM000469(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 — NM000469.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 nm000469 to reproduce the tutorial on this dataset.

Citation

Tao Xie, Sanne J. H. van Rooij, Sai Sun, Noah K. Bryson, Phillip Demarest, … (20). Xie2026 amygdala ablation PTSD: epoched stereo-EEG of the index patient before and after right amygdala radiofrequency ablation (interpretation-bias task). 10.82901/nemar.nm000469

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000469.

Related & sibling datasets

NM000437IEEG · 5 subj NM000472IEEG · 1 subj NM000452IEEG · 1 subj NM000400IEEG · 7 subj NM000462IEEG · 3 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-4.0 · 10.82901/nemar.nm000469

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000469`
Title	Xie2026 amygdala ablation PTSD: epoched stereo-EEG of the index patient before and after right amygdala radiofrequency ablation (interpretation-bias task)
Author (year)	—
Canonical	—
Importable as	`NM000469`
Year	20
Authors	Tao Xie, Sanne J. H. van Rooij, Sai Sun, Noah K. Bryson, Phillip Demarest, Haeorum Park, Luigi Maccotta, Shuo Wang, Peter Brunner, Jon T. Willie
License	CC-BY-NC-4.0
Citation / DOI	10.82901/nemar.nm000469
Source links	OpenNeuro \| NeMAR \| Source URL

NM000469: ieeg dataset, 1 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#