EEGdash›NeMAR›NM000482

Iss. 482 · 7 subjects · 14 recordings · CC-BY-4.0

Dataset Brief · StarkweatherOFC-CCEP

NM000482: ieeg dataset, 7 subjects#

StarkweatherOFC-CCEP: orbitofrontal cortico-cortical evoked potential recordings, 7 patients (Starkweather et al.) (derivative)

Access recordings and metadata through EEGDash.

Citation: Clara Starkweather (—). StarkweatherOFC-CCEP: orbitofrontal cortico-cortical evoked potential recordings, 7 patients (Starkweather et al.) (derivative). 10.82901/nemar.nm000482

Modality: ieeg Subjects: 7 Recordings: 14 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

7-participant iEEG dataset — StarkweatherOFC-CCEP: orbitofrontal cortico-cortical evoked potential recordings, 7 patients (Starkweather et al.) (derivative).

Data & curation Clara Starkweather
Senior author Clara Starkweather

iEEG · 23 (2), 20 (2), 42 (2), 31 (2), 30 (2), 18 (2), 27 (2) ch1000 HzBIDS 1.10.0Task · ccep

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 NM000482

dataset = NM000482(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 = NM000482(cache_dir="./data", subject="01")

Advanced query

dataset = NM000482(
    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{nm000482,
  title = {StarkweatherOFC-CCEP: orbitofrontal cortico-cortical evoked potential recordings, 7 patients (Starkweather et al.) (derivative)},
  author = {Clara Starkweather},
  doi = {10.82901/nemar.nm000482},
  url = {https://doi.org/10.82901/nemar.nm000482},
}

§ 02Study · The README

About This Dataset#

Release: figshare 31083118 “Rapid Homotopic Communication Between Human Orbitofrontal Subregions - dataset and MATLAB codes” (Clara Starkweather; doi:10.6084/m9.figshare.31083118.v2, CC BY 4.0). Release description:

“Data and codes pertaining to the manuscript “Rapid Homotopic Communication Between Human Orbitofrontal Subregions” by Starkweather et al. This is a cortico-cortical evoked potential mapping study. This uploaded data contains preprocessed data timeseries (condensed_ecog_OFC_only.mat) with each item within the structure referring to an individual electrode. condensed_ecog(eRec).bipolar refers to the bipolar referenced data from electrode eRec, and condensed_eocg(eRec).lat_coor corresponds to the medial/lateral distance from the lateral orbital sulcus. The critical data analysis summary (Figure 3a) contained in the manuscript was obtained by first running automate_stim_metadata_collection_zscores.m, followed by CCEP_multiplearrays_multiplesubjects_zscores”

For each participant the release holds condensed_ecog_OFC_only.mat, a struct array with one element per orbitofrontal

electrode contact carrying the authors’ preprocessed timeseries at 1000 Hz: unreferenced_ecog (written here as acq-unreferenced) and bipolar (adjacent-contact re-referenced, acq-bipolar; the first contact of a chain has no bipolar signal and is marked bad). Values are written in microvolts (files whose release values are in volts are multiplied by 1e6; stated per channel in channels.tsv). This is a DERIVATIVE dataset: the raw recordings are not in the release. Channel names are <brain_region><contact> from the release fields. Single-pulse stimulation times and amplitudes (stimset.mat) are in events.tsv (onset = release sample / 1000).

sub-01: 18 OFC contacts (OL), 23.8 min, reference n/a

sub-02: 23 OFC contacts (LOFC, LSFG, ROFC, RSFG), 25.2 min, reference n/a

sub-03: 20 OFC contacts (LOD, ROD), 14.1 min, reference n/a

sub-04: 42 OFC contacts (LOFC, ROFC, RSGC), 90.0 min, reference ROFC1Ref

sub-05: 27 OFC contacts (LOFC, ROFC), 43.0 min, reference ROFC1Ref

sub-06: 30 OFC contacts (LOFC, ROFC), 45.0 min, reference ROFC1Ref

sub-07: 31 OFC contacts (LOFC, ROFC), 23.6 min, reference RACC1Ref

DOI

Orbitofrontal cortico-cortical evoked potential (CCEP) recordings, 7 patients (Starkweather et al.) - derivative

Localisation

No x/y/z coordinates are released. electrodes.tsv carries the release’s sulcus-relative distances per contact (lat_coor = medial/lateral distance from the lateral orbital sulcus per the release description; med_coor, mid_coor, olf_coor, trans_coor copied with their release names) and the release electrode label (brain_region).

sourcedata/figshare-31083118/

All release files per subject folder, byte for byte: condensed_ecog_OFC_only.mat, condensed_ecog_info_only.mat (the files of subjects 04 and 07 are truncated in the release, 2.9 kB / 2.2 kB, and cannot be opened), metadata.mat (CCEP onset latency / peak amplitude / peak latency / significance per stimulated -> recorded pair), stimset.mat, and the authors’ MATLAB scripts under code/.

Provenance

Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_lit138.py; round-trip checks (MNE read-back) in code/conversion_report.json. A different dataset of the same first author (approach-avoidance SEEG HFA) is NEMAR nm000383; participant overlap is unknown.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000482-blue)](https://doi.org/10.82901/nemar.nm000482) # Orbitofrontal cortico-cortical evoked potential (CCEP) recordings, 7 patients (Starkweather et al.) - derivative Release: figshare 31083118 “Rapid Homotopic Communication Between Human Orbitofrontal Subregions - dataset and MATLAB codes” (Clara Starkweather; doi:10.6084/m9.figshare.31083118.v2, CC BY 4.0). Release description: “Data and codes pertaining to the manuscript “Rapid Homotopic Communication Between Human Orbitofrontal Subregions” by Starkweather et al. This is a cortico-cortical evoked potential mapping study. This uploaded data contains preprocessed data timeseries (condensed_ecog_OFC_only.mat) with each item within the structure referring to an individual electrode. condensed_ecog(eRec).bipolar refers to the bipolar referenced data from electrode eRec, and condensed_eocg(eRec).lat_coor corresponds to the medial/lateral distance from the lateral orbital sulcus. The critical data analysis summary (Figure 3a) contained in the manuscript was obtained by first running automate_stim_metadata_collection_zscores.m, followed by CCEP_multiplearrays_multiplesubjects_zscores” ## Content (7 participants, 4.41 h per acquisition) For each participant the release holds condensed_ecog_OFC_only.mat, a struct array with one element per orbitofrontal electrode contact carrying the authors’ preprocessed timeseries at 1000 Hz: unreferenced_ecog (written here as acq-unreferenced) and bipolar (adjacent-contact re-referenced, acq-bipolar; the first contact of a chain has no bipolar signal and is marked bad). Values are written in microvolts (files whose release values are in volts are multiplied by 1e6; stated per channel in channels.tsv). This is a DERIVATIVE dataset: the raw recordings are not in the release. Channel names are <brain_region><contact> from the release fields. Single-pulse stimulation times and amplitudes (stimset.mat) are in events.tsv (onset = release sample / 1000).

sub-01: 18 OFC contacts (OL), 23.8 min, reference n/a

sub-02: 23 OFC contacts (LOFC, LSFG, ROFC, RSFG), 25.2 min, reference n/a

sub-03: 20 OFC contacts (LOD, ROD), 14.1 min, reference n/a

sub-04: 42 OFC contacts (LOFC, ROFC, RSGC), 90.0 min, reference ROFC1Ref

sub-05: 27 OFC contacts (LOFC, ROFC), 43.0 min, reference ROFC1Ref

sub-06: 30 OFC contacts (LOFC, ROFC), 45.0 min, reference ROFC1Ref

sub-07: 31 OFC contacts (LOFC, ROFC), 23.6 min, reference RACC1Ref

## Localisation No x/y/z coordinates are released. electrodes.tsv carries the release’s sulcus-relative distances per contact (lat_coor = medial/lateral distance from the lateral orbital sulcus per the release description; med_coor, mid_coor, olf_coor, trans_coor copied with their release names) and the release electrode label (brain_region). ## sourcedata/figshare-31083118/ All release files per subject folder, byte for byte: condensed_ecog_OFC_only.mat, condensed_ecog_info_only.mat (the files of subjects 04 and 07 are truncated in the release, 2.9 kB / 2.2 kB, and cannot be opened), metadata.mat (CCEP onset latency / peak amplitude / peak latency / significance per stimulated -> recorded pair), stimset.mat, and the authors’ MATLAB scripts under code/. ## Provenance Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_lit138.py; round-trip checks (MNE read-back) in code/conversion_report.json. A different dataset of the same first author (approach-avoidance SEEG HFA) is NEMAR nm000383; participant overlap is unknown.

License: CC-BY-4.0

Authors:

Clara Starkweather

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000482

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

18202327303142

Sampling frequencies: 1000.0 Hz (n=14 recordings)

Total recording duration: 8 h 49 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 23 (2), 20 (2), 42 (2), 31 (2), 30 (2), 18 (2), 27 (2) ch · iEEG · 1000 Hz · 7 subjects, 14 recordings

Live trace viewer — sub-04 · task-ccep

Showing one representative recording out of 7 subjects and 14 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.NM000482(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000482

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

class eegdash.dataset.NM000482(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

StarkweatherOFC-CCEP: orbitofrontal cortico-cortical evoked potential recordings, 7 patients (Starkweather et al.) (derivative)

Study:: nm000482 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000482.

Modality: ieeg; Subject type: Unknown. Subjects: 7; recordings: 14; 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/nm000482 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000482 DOI: https://doi.org/10.82901/nemar.nm000482

Examples

>>> from eegdash.dataset import NM000482
>>> dataset = NM000482(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 — NM000482.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 nm000482 to reproduce the tutorial on this dataset.

Citation

Clara Starkweather (n.d.). StarkweatherOFC-CCEP: orbitofrontal cortico-cortical evoked potential recordings, 7 patients (Starkweather et al.) (derivative). 10.82901/nemar.nm000482

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000482.

Related & sibling datasets

NM000473IEEG · 10 subj NM000480IEEG · 11 subj NM000460IEEG · 1 subj NM000405IEEG · 16 subj NM000432IEEG · 18 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000482

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000482`
Title	StarkweatherOFC-CCEP: orbitofrontal cortico-cortical evoked potential recordings, 7 patients (Starkweather et al.) (derivative)
Author (year)	—
Canonical	—
Importable as	`NM000482`
Year	—
Authors	Clara Starkweather
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000482
Source links	OpenNeuro \| NeMAR \| Source URL

NM000482: ieeg dataset, 7 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#