NM000476: ieeg dataset, 5 subjects#
Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery)
Access recordings and metadata through EEGDash.
Citation: Colin W. Hoy, Coralie de Hemptinne, Sarah S. Wang, Catherine J. Harmer, Matthew A. J. Apps, Masud Husain, Philip A. Starr, Simon Little (20). Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery). 10.82901/nemar.nm000476
Modality: ieeg Subjects: 5 Recordings: 14 License: CC-BY-4.0 Source: nemar
Metadata: Complete (100%)
5-participant iEEG dataset — Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000476
dataset = NM000476(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000476(cache_dir="./data", subject="01")
Advanced query
dataset = NM000476(
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{nm000476,
title = {Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery)},
author = {Colin W. Hoy and Coralie de Hemptinne and Sarah S. Wang and Catherine J. Harmer and Matthew A. J. Apps and Masud Husain and Philip A. Starr and Simon Little},
doi = {10.82901/nemar.nm000476},
url = {https://doi.org/10.82901/nemar.nm000476},
}
About This Dataset#
5 subjects, 14 recordings, 3.90 h. Released by the authors on OSF (doi:10.17605/OSF.IO/JQ6Z4, CC-BY-4.0).
-2 to 9 s around the offer, 422 or 500 Hz) with the two channel labels of the release (STN or GPi; FPC or OFC), written
back-to-back with one New Segment marker per trial. events.tsv carries every per-trial field of sbj_data.bhv (key, rt,
block, effort, stake, decision, previous-trial values, model values SV/EFFs/p_accept/dec_ease, …); code/*_bhvextra.json holds the
index lists (good_key_ix, reject_ix, …) and the model parameters (sbj_data.mdl).
Hoy et al. 2024: basal ganglia and prefrontal LFP during effort-based decisions (intraoperative DBS recordings)
*_acq-raw_run-<k>_ieeg.vhdr(all five subjects, two runs each): the continuous neural exports ofraw_data/(signal2 x N at 422 Hz, ordatarN x 4 at 500 Hz for PFC01) written unchanged. The release does not document the channel order or units of these exports (channelsch1..,unitsn/a) and the behavioural trial timestamps are on a separate clock (sync files), so these runs have no events.tsv.No electrode coordinates are released (
electrodes.tsvwith n/a). Age and sex are not in the release.sourcedata/osf-jq6z4/: the preprocessed and neural MAT files (MAT header export dates set to day 01). The behavioural and sync files contain exact session timestamps and are not redistributed (seeNOT_COPIED_release_files.txt); session months: {“PFC01_behavioral_data_run1.mat”: “2020-06-01”, “PFC01_behavioral_data_run2.mat”: “2020-06-01”, “PFC03_behavioral_data_run1.mat”: “2019-12-01”, “PFC03_behavioral_data_run2.mat”: “2019-12-01”, “PFC04_behavioral_data_run1.mat”: “2019-09-01”, “PFC04_behavioral_data_run2.mat”: “2019-09-01”, “PFC05_behavioral_data_run1.mat”: “2019-09-01”, “PFC05_behavioral_data_run2.mat”: “2019-09-01”, “PMC10_behavioral_data_run1.mat”: “2020-01-01”, “PMC10_behavioral_data_run2.mat”: “2020-01-01”}.
Privacy
Subject codes only; the behavioural files with exact timestamps are excluded. String scan of the converted MAT files: 12 flagged strings (see code/conversion_report.json).
NEMAR Metadata#
[](https://doi.org/10.82901/nemar.nm000476) # Hoy et al. 2024: basal ganglia and prefrontal LFP during effort-based decisions (intraoperative DBS recordings) 5 subjects, 14 recordings, 3.90 h. Released by the authors on OSF (doi:10.17605/OSF.IO/JQ6Z4, CC-BY-4.0). ## Organisation (DatasetType derivative) - *_acq-preproc_ieeg.vhdr (PFC01, PFC03, PFC04, PFC05): the authors’ preprocessed, epoched bipolar signals (sbj_data.ts, trials
-2 to 9 s around the offer, 422 or 500 Hz) with the two channel labels of the release (STN or GPi; FPC or OFC), written back-to-back with one New Segment marker per trial. events.tsv carries every per-trial field of sbj_data.bhv (key, rt, block, effort, stake, decision, previous-trial values, model values SV/EFFs/p_accept/dec_ease, …); code/*_bhvextra.json holds the index lists (good_key_ix, reject_ix, …) and the model parameters (sbj_data.mdl).
*_acq-raw_run-<k>_ieeg.vhdr (all five subjects, two runs each): the continuous neural exports of raw_data/ (signal 2 x N at 422 Hz, or datar N x 4 at 500 Hz for PFC01) written unchanged. The release does not document the channel order or units of these exports (channels ch1.., units n/a) and the behavioural trial timestamps are on a separate clock (sync files), so these runs have no events.tsv.
No electrode coordinates are released (electrodes.tsv with n/a). Age and sex are not in the release.
sourcedata/osf-jq6z4/: the preprocessed and neural MAT files (MAT header export dates set to day 01). The behavioural and sync files contain exact session timestamps and are not redistributed (see NOT_COPIED_release_files.txt); session months: {“PFC01_behavioral_data_run1.mat”: “2020-06-01”, “PFC01_behavioral_data_run2.mat”: “2020-06-01”, “PFC03_behavioral_data_run1.mat”: “2019-12-01”, “PFC03_behavioral_data_run2.mat”: “2019-12-01”, “PFC04_behavioral_data_run1.mat”: “2019-09-01”, “PFC04_behavioral_data_run2.mat”: “2019-09-01”, “PFC05_behavioral_data_run1.mat”: “2019-09-01”, “PFC05_behavioral_data_run2.mat”: “2019-09-01”, “PMC10_behavioral_data_run1.mat”: “2020-01-01”, “PMC10_behavioral_data_run2.mat”: “2020-01-01”}.
## Privacy Subject codes only; the behavioural files with exact timestamps are excluded. String scan of the converted MAT files: 12 flagged strings (see code/conversion_report.json).
License: CC-BY-4.0
Authors:
Colin W. Hoy
Coralie de Hemptinne
Sarah S. Wang
Catherine J. Harmer
Matthew A. J. Apps
… and 3 more
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 3 h 53 min
Signal · Electrodes & live trace#
Live trace viewer — sub-PFC03 · task-effort · run-2
Showing one representative recording out of
5 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
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 |
Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery) |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Colin W. Hoy, Coralie de Hemptinne, Sarah S. Wang, Catherine J. Harmer, Matthew A. J. Apps, Masud Husain, Philip A. Starr, Simon Little |
License |
CC-BY-4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000476,
title = {Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery)},
author = {Colin W. Hoy and Coralie de Hemptinne and Sarah S. Wang and Catherine J. Harmer and Matthew A. J. Apps and Masud Husain and Philip A. Starr and Simon Little},
doi = {10.82901/nemar.nm000476},
url = {https://doi.org/10.82901/nemar.nm000476},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000476(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery)
- Study:
nm000476(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000476.Modality:
ieeg; Subject type:Unknown. Subjects: 5; 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
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/nm000476 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000476 DOI: https://doi.org/10.82901/nemar.nm000476
Examples
>>> from eegdash.dataset import NM000476 >>> dataset = NM000476(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 nm000476 to reproduce the tutorial on this dataset.
Citation
Colin W. Hoy, Coralie de Hemptinne, Sarah S. Wang, Catherine J. Harmer, Matthew A. J. Apps, … (20). Hoy2024 effort decision LFP: subthalamic/pallidal and prefrontal recordings during an effort-based decision task (DBS surgery). 10.82901/nemar.nm000476
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000476.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset