NM000477: ieeg dataset, 24 subjects#
DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025)
Access recordings and metadata through EEGDash.
Citation: Arianna Davis, Ofer Perl, Brian Kopell, Matthew Heflin, Salman Qasim, Zarghona Imtiaz, Ayaka Kato, Soojung Na, Navid Mikail, Helen S. Mayberg, P. Read Montague, Ignacio Saez, Xiaosi Gu (20). DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025). 10.82901/nemar.nm000477
Modality: ieeg Subjects: 24 Recordings: 67 License: CC-BY-4.0 Source: nemar
Metadata: Complete (100%)
24-participant iEEG dataset — DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025).
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import NM000477
dataset = NM000477(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = NM000477(cache_dir="./data", subject="01")
Advanced query
dataset = NM000477(
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{nm000477,
title = {DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025)},
author = {Arianna Davis and Ofer Perl and Brian Kopell and Matthew Heflin and Salman Qasim and Zarghona Imtiaz and Ayaka Kato and Soojung Na and Navid Mikail and Helen S. Mayberg and P. Read Montague and Ignacio Saez and Xiaosi Gu},
doi = {10.82901/nemar.nm000477},
url = {https://doi.org/10.82901/nemar.nm000477},
}
About This Dataset#
Release: figshare 29082671 “Single neurons in the human substantia nigra encode social learning signals” (Arianna Davis, Ofer Perl, Brian Kopell, Matthew Heflin, Salman Qasim, Zarghona Imtiaz, Ayaka Kato, Soojung Na, Navid Mikail, Helen S. Mayberg, P. Read Montague, Ignacio Saez, Xiaosi Gu;
doi:10.6084/m9.figshare.29082671.v2, CC BY 4.0). Release description (excerpt): “Description of the data and file structure Behavioral and electrophysiological data were collected from neurosurgical patients who played a social exchange game while undergoing single-unit electrophysiological recording from the substantia nigra and globus pallidus in order to delineate the potential roles of these neurons in encoding social learning signals. Files and variables File: Code.zip Description: This compressed folder contains all scripts required to conduct the behavioral and neural analyses presented in the paper. It is organized into two subfolders: behavior and neural. The behavior folder is further organized into a prep folder and a modeling folder. The scripts in the prep folder are what were used for data cleaning, organizing, and model-free analyses. All model scripts are in the modeling folder. Scripts for carrying out neural analyses are in the neural folder. …”
Each Alpha Omega file of the release (one microelectrode position =
run-<file number>) is one BrainVision recording with
the wideband RAW streams at 44 kHz: the microelectrode tip (<side>T<track>micro<nn>, Alpha Omega CRAW_nn) and the macro
contact of the same lead (...macro<nn>, CMacro_RAW_nn), int16 counts x BitResolution / Gain in microvolts (float32;
the exact factors are in channels.tsv). Where a file’s streams differ in length by a few samples they are cut to the
shortest (listed in code/conversion_report.json). Sessions: ses-1 = release folder ptNNNN, ses-2 = ptNNNN_2; the
side, track and drive depth of every run come from the Alpha Omega file name (LT1D-0.490F0002 = left track 1, depth
-0.490 mm, file 2) and are in *_scans.tsv. Target (substantia nigra / globus pallidus) from the release folders.
events.tsv: rising/falling edges of the digital inputs (task synchronisation pulses).
Intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025)
Not converted (kept in sourcedata/figshare-29082671/): the online LFP (CLFP, CMacro_LFP, 1.375 kHz) and spike-band
(CSPK) streams, threshold spike segments (CSEG), analog inputs, the behaviour logs (behavior/*.json.txt, *.csv), the
OSORT cluster-to-unit merge sheets (merge_pt*.xlsx) and the Alpha Omega MAT files themselves (byte-identical except the
MAT header creation day set to 01). The release’s Code.zip (analysis scripts) and the unit waveform figures
(Data_SN_morph-unit-plots.zip) are not included; they remain in the figshare record.
Localisation
No coordinates are released. electrodes.tsv lists the microelectrode/macro channels per session with the target structure;
hemisphere, track and depth per run are in scans.tsv.
Provenance
Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_lit9.py (round-trip checks in
code/conversion_report.json).
NEMAR Metadata#
[](https://doi.org/10.82901/nemar.nm000477) # Intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025) Release: figshare 29082671 “Single neurons in the human substantia nigra encode social learning signals” (Arianna Davis, Ofer Perl, Brian Kopell, Matthew Heflin, Salman Qasim, Zarghona Imtiaz, Ayaka Kato, Soojung Na, Navid Mikail, Helen S. Mayberg, P. Read Montague, Ignacio Saez, Xiaosi Gu; doi:10.6084/m9.figshare.29082671.v2, CC BY 4.0). Release description (excerpt): “Description of the data and file structure Behavioral and electrophysiological data were collected from neurosurgical patients who played a social exchange game while undergoing single-unit electrophysiological recording from the substantia nigra and globus pallidus in order to delineate the potential roles of these neurons in encoding social learning signals. Files and variables File: Code.zip Description: This compressed folder contains all scripts required to conduct the behavioral and neural analyses presented in the paper. It is organized into two subfolders: behavior and neural. The behavior folder is further organized into a prep folder and a modeling folder. The scripts in the prep folder are what were used for data cleaning, organizing, and model-free analyses. All model scripts are in the modeling folder. Scripts for carrying out neural analyses are in the neural folder. …” ## Content (67 runs, 5.19 h, 24 participants, 33 sessions) Each Alpha Omega file of the release (one microelectrode position = run-<file number>) is one BrainVision recording with the wideband RAW streams at 44 kHz: the microelectrode tip (<side>T<track>micro<nn>, Alpha Omega CRAW_nn) and the macro contact of the same lead (…macro<nn>, CMacro_RAW_nn), int16 counts x BitResolution / Gain in microvolts (float32; the exact factors are in channels.tsv). Where a file’s streams differ in length by a few samples they are cut to the shortest (listed in code/conversion_report.json). Sessions: ses-1 = release folder ptNNNN, ses-2 = ptNNNN_2; the side, track and drive depth of every run come from the Alpha Omega file name (LT1D-0.490F0002 = left track 1, depth -0.490 mm, file 2) and are in *_scans.tsv. Target (substantia nigra / globus pallidus) from the release folders. events.tsv: rising/falling edges of the digital inputs (task synchronisation pulses). Not converted (kept in sourcedata/figshare-29082671/): the online LFP (CLFP, CMacro_LFP, 1.375 kHz) and spike-band (CSPK) streams, threshold spike segments (CSEG), analog inputs, the behaviour logs (behavior/*.json.txt, *.csv), the OSORT cluster-to-unit merge sheets (merge_pt*.xlsx) and the Alpha Omega MAT files themselves (byte-identical except the MAT header creation day set to 01). The release’s Code.zip (analysis scripts) and the unit waveform figures (Data_SN_morph-unit-plots.zip) are not included; they remain in the figshare record. ## Localisation No coordinates are released. electrodes.tsv lists the microelectrode/macro channels per session with the target structure; hemisphere, track and depth per run are in scans.tsv. ## Provenance Converted by the ieeg-nemar-20261005 campaign (lane o5c-B) with code/o5c_B_conv_lit9.py (round-trip checks in code/conversion_report.json).
License: CC-BY-4.0
Authors:
Arianna Davis
Ofer Perl
Brian Kopell
Matthew Heflin
Salman Qasim
… and 8 more
Versions:
Version |
DOI |
Released |
|---|---|---|
|
Cohort#
Dataset Statistics#
Channel counts: 2 ch (n=67 recordings)
Sampling frequencies: 44000.0 Hz (n=67 recordings)
Total recording duration: 5 h 11 min
Signal · Electrodes & live trace#
Live trace viewer — sub-0268 · ses-2 · task-ultimatum · run-03
Showing one representative recording out of
24 subjects and 67 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 |
DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025) |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Arianna Davis, Ofer Perl, Brian Kopell, Matthew Heflin, Salman Qasim, Zarghona Imtiaz, Ayaka Kato, Soojung Na, Navid Mikail, Helen S. Mayberg, P. Read Montague, Ignacio Saez, Xiaosi Gu |
License |
CC-BY-4.0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{nm000477,
title = {DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025)},
author = {Arianna Davis and Ofer Perl and Brian Kopell and Matthew Heflin and Salman Qasim and Zarghona Imtiaz and Ayaka Kato and Soojung Na and Navid Mikail and Helen S. Mayberg and P. Read Montague and Ignacio Saez and Xiaosi Gu},
doi = {10.82901/nemar.nm000477},
url = {https://doi.org/10.82901/nemar.nm000477},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.NM000477(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025)
- Study:
nm000477(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
NM000477.Modality:
ieeg; Subject type:Unknown. Subjects: 24; recordings: 67; 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/nm000477 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000477 DOI: https://doi.org/10.82901/nemar.nm000477
Examples
>>> from eegdash.dataset import NM000477 >>> dataset = NM000477(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 nm000477 to reproduce the tutorial on this dataset.
Citation
Arianna Davis, Ofer Perl, Brian Kopell, Matthew Heflin, Salman Qasim, … (20). DavisSN-UG: intraoperative microelectrode recordings in human substantia nigra and globus pallidus during a social ultimatum game (Davis et al., 2025). 10.82901/nemar.nm000477
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.nm000477.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset