EEGdash›NeMAR›NM000383

Iss. 383 · 6 subjects · 30 recordings · CC0-1.0

Dataset Brief · Medial and lateral orbitofrontal cortex in approach-avoidance…

NM000383: ieeg dataset, 6 subjects#

Medial and lateral orbitofrontal cortex in approach-avoidance decisions (Starkweather et al., 2026): SEEG high-frequency activity, 6 patients (derivative)

Access recordings and metadata through EEGDash.

Citation: Clara Kwon Starkweather, Ethan H. Willbrand, Kristin K. Sellers, Patrick W. Hullett, Andrew D. Krystal, A. Moses Lee, Jon T. Willie, Peter Brunner, Ming Hsu, Edward F. Chang, Robert T. Knight (2026). Medial and lateral orbitofrontal cortex in approach-avoidance decisions (Starkweather et al., 2026): SEEG high-frequency activity, 6 patients (derivative). 10.82901/nemar.nm000383

Modality: ieeg Subjects: 6 Recordings: 30 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

6-participant iEEG dataset — Medial and lateral orbitofrontal cortex in approach-avoidance decisions (Starkweather et al., 2026): SEEG high-frequency activity, 6 patients (derivative).

Data & curation Clara Kwon Starkweather · Ethan H. Willbrand · Kristin K. Sellers · Patrick W. Hullett · Andrew D. Krystal · A. Moses Lee · …
Senior author Robert T. Knight
Year 2026 · Distributed via NeMAR

iEEG · 17 (10), 22 (5), 37 (5), 20 (5), 16 (5) ch1000 HzBIDS 1.10.0Task · approachavoid

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 NM000383

dataset = NM000383(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 = NM000383(cache_dir="./data", subject="01")

Advanced query

dataset = NM000383(
    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{nm000383,
  title = {Medial and lateral orbitofrontal cortex in approach-avoidance decisions (Starkweather et al., 2026): SEEG high-frequency activity, 6 patients (derivative)},
  author = {Clara Kwon Starkweather and Ethan H. Willbrand and Kristin K. Sellers and Patrick W. Hullett and Andrew D. Krystal and A. Moses Lee and Jon T. Willie and Peter Brunner and Ming Hsu and Edward F. Chang and Robert T. Knight},
  doi = {10.82901/nemar.nm000383},
  url = {https://doi.org/10.82901/nemar.nm000383},
}

§ 02Study · The README

About This Dataset#

DERIVATIVE dataset. The Dryad release contains the authors’ processed high-frequency activity (HFA, 70-150 Hz), not raw iEEG.

(version 5, 2026-09-09). License CC0 1.0.

DOI

Approach-avoidance decisions in human orbitofrontal cortex: SEEG high-frequency activity (derivative)

Article: Starkweather et al. (2026) Nature Neuroscience, doi:10.1038/s41593-026-02444-4 (open access).
Analysis code: cstarkweather/OFC_analysis_codes. Task: cstarkweather/Starkweather-neurogame
Original files unchanged in sourcedata/dryad-kh18932k7/ (subject.mat, behavior_data.mat, README.md).

Participants and ethics

View full README

DOI

Approach-avoidance decisions in human orbitofrontal cortex: SEEG high-frequency activity (derivative)

Article: Starkweather et al. (2026) Nature Neuroscience, doi:10.1038/s41593-026-02444-4 (open access).
Analysis code: cstarkweather/OFC_analysis_codes. Task: cstarkweather/Starkweather-neurogame
Original files unchanged in sourcedata/dryad-kh18932k7/ (subject.mat, behavior_data.mat, README.md).

Participants and ethics

Six patients with stereotactic depth electrodes (five implanted at UCSF, one at WUSTL), implanted for epilepsy, MDD or OCD.

Ethics (verbatim from the article): “Approval for the study was granted by the institutional review boards of the University of California, San Francisco (UCSF), University of California, Berkeley and Washington University in St. Louis (WUSTL). Written informed consent was obtained from all participants prior to testing.” Age, sex and clinical indication come from Extended Data Table 1 of the article, matched to the release by subject order (see participants.json for the caveat).

Files

sub-XX/ieeg/sub-XX_task-approachavoid_acq-<align>_ieeg.*: BrainVision float32. One file per alignment in the release (electrode(k).trigger(1..5)): onset (trigger 1, trial onset) and decision (trigger 2, button press) as described by the README; trig3, trig4, trig5 are present in the release but not described, and are kept as released. Each trial is a 12000-sample segment at 1 kHz with time zero at sample 5000 (-4.999 to +7.000 s; the authors’ code uses column 5000 as time zero). The release README mentions 10000 columns; the files hold 12000. Values are unchanged (units not stated: n/a).
*_events.tsv: one row per trial: segment boundaries, alignment time, and per-trial behaviour (choice = release field decision, rt, outcome, offers, trial type, approach probability, conflict, value) plus the raw triggers values, all verbatim.
*_goodtrials.tsv: electrode(k).trigger(j).good_trials per channel and trial (1 = kept by the authors’ artifact review).
*_channels.tsv: release electrodes (E01..), bipolar, with the release’s anatomical label, array value and sulcal coordinates.
sub-XX/sub-XX_trialtypes.tsv: per-trial-type tables (offers, approach probability, conflict, value). sub-XX_VAS.tsv: VAS field.
No x/y/z electrode coordinates are released (electrodes.tsv has n/a).

Notes

The release README says 100 electrodes remained after exclusions; the subject.mat holds 129 electrode entries in total (16, 17, 37, 22, 17, 20). All are included as released; array and good_trials are kept for filtering.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000383-blue)](https://doi.org/10.82901/nemar.nm000383) # Approach-avoidance decisions in human orbitofrontal cortex: SEEG high-frequency activity (derivative) DERIVATIVE dataset. The Dryad release contains the authors’ processed high-frequency activity (HFA, 70-150 Hz), not raw iEEG. ## Source - Dryad: Starkweather, Willbrand, Sellers, Hullett, Krystal, Lee, Willie, Brunner, Hsu, Chang, Knight. doi:10.5061/dryad.kh18932k7

(version 5, 2026-09-09). License CC0 1.0.

Article: Starkweather et al. (2026) Nature Neuroscience, doi:10.1038/s41593-026-02444-4 (open access).
Analysis code: cstarkweather/OFC_analysis_codes. Task: cstarkweather/Starkweather-neurogame
Original files unchanged in sourcedata/dryad-kh18932k7/ (subject.mat, behavior_data.mat, README.md).

## Participants and ethics Six patients with stereotactic depth electrodes (five implanted at UCSF, one at WUSTL), implanted for epilepsy, MDD or OCD. Ethics (verbatim from the article): “Approval for the study was granted by the institutional review boards of the University of California, San Francisco (UCSF), University of California, Berkeley and Washington University in St. Louis (WUSTL). Written informed consent was obtained from all participants prior to testing.” Age, sex and clinical indication come from Extended Data Table 1 of the article, matched to the release by subject order (see participants.json for the caveat). ## Files - sub-XX/ieeg/sub-XX_task-approachavoid_acq-<align>_ieeg.*: BrainVision float32. One file per alignment in the release

(electrode(k).trigger(1..5)): onset (trigger 1, trial onset) and decision (trigger 2, button press) as described by the README; trig3, trig4, trig5 are present in the release but not described, and are kept as released. Each trial is a 12000-sample segment at 1 kHz with time zero at sample 5000 (-4.999 to +7.000 s; the authors’ code uses column 5000 as time zero). The release README mentions 10000 columns; the files hold 12000. Values are unchanged (units not stated: n/a).

*_events.tsv: one row per trial: segment boundaries, alignment time, and per-trial behaviour (choice = release field decision, rt, outcome, offers, trial type, approach probability, conflict, value) plus the raw triggers values, all verbatim.
*_goodtrials.tsv: electrode(k).trigger(j).good_trials per channel and trial (1 = kept by the authors’ artifact review).
*_channels.tsv: release electrodes (E01..), bipolar, with the release’s anatomical label, array value and sulcal coordinates.
sub-XX/sub-XX_trialtypes.tsv: per-trial-type tables (offers, approach probability, conflict, value). sub-XX_VAS.tsv: VAS field.
No x/y/z electrode coordinates are released (electrodes.tsv has n/a).

## Notes - The release README says 100 electrodes remained after exclusions; the subject.mat holds 129 electrode entries in total

(16, 17, 37, 22, 17, 20). All are included as released; array and good_trials are kept for filtering.

License: CC0-1.0

Authors:

Clara Kwon Starkweather
Ethan H. Willbrand
Kristin K. Sellers
Patrick W. Hullett
Andrew D. Krystal
… and 6 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000383

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=6, range 21–46 yr, mean 28.2 yr)

202545

Female · 2Male · 4

Sex composition

Female
2
Male
4
F : M ratio
0.50 : 1

33% female · n = 6 subjects with reported sex.

Channel counts (ch)

1617202237

Sampling frequencies: 1000.0 Hz (n=30 recordings)

Total recording duration: 21 h 4 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 17 (10), 22 (5), 37 (5), 20 (5), 16 (5) ch · iEEG · 1000 Hz · 6 subjects, 30 recordings

Live trace viewer — sub-04 · task-approachavoid

Showing one representative recording out of 6 subjects and 30 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.NM000383(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000383

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

class eegdash.dataset.NM000383(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Medial and lateral orbitofrontal cortex in approach-avoidance decisions (Starkweather et al., 2026): SEEG high-frequency activity, 6 patients (derivative)

Study:: nm000383 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000383.

Modality: ieeg; Subject type: Unknown. Subjects: 6; recordings: 30; 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/nm000383 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000383 DOI: https://doi.org/10.82901/nemar.nm000383

Examples

>>> from eegdash.dataset import NM000383
>>> dataset = NM000383(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 — NM000383.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 nm000383 to reproduce the tutorial on this dataset.

Citation

Clara Kwon Starkweather, Ethan H. Willbrand, Kristin K. Sellers, Patrick W. Hullett, Andrew D. Krystal, … (2026). Medial and lateral orbitofrontal cortex in approach-avoidance decisions (Starkweather et al., 2026): SEEG high-frequency activity, 6 patients (derivative). 10.82901/nemar.nm000383

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000383.

Related & sibling datasets

NM000350IEEG · 6 subj NM000387IEEG · 9 subj NM000399IEEG · 11 subj NM000394IEEG · 1 subj NM000386IEEG · 6 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000383

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000383`
Title	Medial and lateral orbitofrontal cortex in approach-avoidance decisions (Starkweather et al., 2026): SEEG high-frequency activity, 6 patients (derivative)
Author (year)	—
Canonical	—
Importable as	`NM000383`
Year	2026
Authors	Clara Kwon Starkweather, Ethan H. Willbrand, Kristin K. Sellers, Patrick W. Hullett, Andrew D. Krystal, A. Moses Lee, Jon T. Willie, Peter Brunner, Ming Hsu, Edward F. Chang, Robert T. Knight
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000383
Source links	OpenNeuro \| NeMAR \| Source URL

NM000383: ieeg dataset, 6 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#