EEGdash›NeMAR›NM000429

Iss. 429 · 8 subjects · 16 recordings · CC0-1.0

Dataset Brief · Human subthalamic nucleus microelectrode recordings during an…

NM000429: ieeg dataset, 8 subjects#

Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021)

Access recordings and metadata through EEGDash.

Citation: Dennis London, Arash Fazl, Kalman Katlowitz, Marisol Soula, Michael Pourfar, Alon Mogilner, Roozbeh Kiani (2021). Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021). 10.82901/nemar.nm000429

Modality: ieeg Subjects: 8 Recordings: 16 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

8-participant iEEG dataset — Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021).

Data & curation Dennis London · Arash Fazl · Kalman Katlowitz · Marisol Soula · Michael Pourfar · Alon Mogilner · …
Senior author Roozbeh Kiani
Year 2021 · Distributed via NeMAR
Funding NIMH R01 MH109180 · McKnight Scholar Award · + 2 more

iEEG · 4 (10), 2 (5), 5 ch44000 Hz · mixedBIDS 1.10.0Task · interruptedreach

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 NM000429

dataset = NM000429(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 = NM000429(cache_dir="./data", subject="01")

Advanced query

dataset = NM000429(
    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{nm000429,
  title = {Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021)},
  author = {Dennis London and Arash Fazl and Kalman Katlowitz and Marisol Soula and Michael Pourfar and Alon Mogilner and Roozbeh Kiani},
  doi = {10.82901/nemar.nm000429},
  url = {https://doi.org/10.82901/nemar.nm000429},
}

§ 02Study · The README

About This Dataset#

Intraoperative microelectrode recordings from the subthalamic nucleus (STN) of patients with Parkinson’s disease during

deep brain stimulation surgery (NYU Langone), while they performed an extended reaching task on a touchscreen in which the target sometimes changed during the movement (“interrupted reach”). Article: subjects were off anti-Parkinsonian medication; one or two Neuroprobe Sonus tungsten microelectrodes (1 MOhm, Alpha Omega), 2 mm apart, driven on one microdrive; recordings referenced to the outer cannula; raw signals sampled at 44 kHz.

subthalamic nucleus. doi:10.5061/dryad.2jm63xsq2. License CC0 1.0.

DOI

Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021)

Article: eLife 10:e64893 (2021), doi:10.7554/eLife.64893. Code: https://github.com/dlondon12/InterruptedReachClustMass
The 8 subject zips were downloaded through the Dryad API on Voyager and matched the Dryad SHA-256 digests. Not mirrored: CWT_mean.zip, Hilbert_beta_mean.zip, PPC_*.zip (time-frequency and phase-consistency derivatives) and processed_data.mat (spiking/behaviour summary); they remain on Dryad.

Contents

View full README

DOI

Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021)

Article: eLife 10:e64893 (2021), doi:10.7554/eLife.64893. Code: https://github.com/dlondon12/InterruptedReachClustMass
The 8 subject zips were downloaded through the Dryad API on Voyager and matched the Dryad SHA-256 digests. Not mirrored: CWT_mean.zip, Hilbert_beta_mean.zip, PPC_*.zip (time-frequency and phase-consistency derivatives) and processed_data.mat (spiking/behaviour summary); they remain on Dryad.

Contents

sub-<nn>/ieeg/sub-<nn>_task-interruptedreach_run-<block>_ieeg.*: 16 task blocks of 8 subjects (2.57 h). run = the release block number. Channels: CRAW_xx = microelectrode tip, CMacro_RAW_xx = macro contact of the same lead, wideband RAW streams at the acquisition rate (44 kHz; Sub04: 22 kHz, rate from the stored sample count and begin/end times). Values: int16 x BitResolution / Gain (both stored per channel) in µV, float32.
events.tsv: digital port words (CPORT__1) and digital-input rising edges (CDIG_IN_x) on the RAW time axis; see task-interruptedreach_events.json for the time convention. Trial-level behaviour (touch trajectories, trial types) is in the behaviour .mat files in sourcedata.
sourcedata/dryad-2jm63xsq2/SubXX/BlockYY/: every file of the subject zips: the complete Alpha Omega export (also the online LFP streams at 1.375 kHz, spike segments, digital channels), the behaviour/task .mat and the Plexon spike-time .txt files (sorted units). Changes: MAT-file header dates set to day 01; file names with patient initials and a date were renamed (4 files: Sub02_OR_HandR_STNL_Block02.mat, Sub09_HandR_STNL_Block02.mat, plexon_Sub02_OR_HandR_STNL_Block02_spikes1.txt, plexon_Sub02_OR_HandR_STNL_Block02_spikes2.txt).

Timing caveat

Sampling rate = the nominal rate stored per channel (_KHz). In some exports the stored begin/end times span more time than samples / rate, which indicates recording pauses concatenated in the export; event onsets there are exact only up to the first pause: Sub02/Block01: 29792526 samples over TimeBegin-TimeEnd 1634.2 s (18231.1 Hz vs nominal 44000 Hz); Sub04/Block01: 8873514 samples over TimeBegin-TimeEnd 172.2 s (51522.8 Hz vs nominal 22000 Hz); Sub11/Block01: 29672592 samples over TimeBegin-TimeEnd 133.8 s (221785.2 Hz vs nominal 44000 Hz); Sub11/Block05: 26669440 samples over TimeBegin-TimeEnd 139.9 s (190625.3 Hz vs nominal 44000 Hz); Sub11/Block06: 28558928 samples over TimeBegin-TimeEnd 138.0 s (207009.3 Hz vs nominal 44000 Hz); Sub11/Block07: 27740369 samples over TimeBegin-TimeEnd 135.2 s (205178.1 Hz vs nominal 44000 Hz); Sub13/Block02: 30603712 samples over TimeBegin-TimeEnd 139.1 s (219961.2 Hz vs nominal 44000 Hz).

Localisation

Recordings were from the STN (article); the side comes from the file names (STNL/STNR). No coordinates or depths are in the release; electrodes.tsv lists the channels with x, y, z = n/a.

Participants

Age/sex per patient are in the article’s Supplementary file 1, which is not linked to subject numbers in the release; n/a here.

Privacy

File names with initials and dates were renamed; MAT headers keep year and month only. String scan of the behaviour .mat files: no names, dates or identifiers found.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000429-blue)](https://doi.org/10.82901/nemar.nm000429) # Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021) Intraoperative microelectrode recordings from the subthalamic nucleus (STN) of patients with Parkinson’s disease during deep brain stimulation surgery (NYU Langone), while they performed an extended reaching task on a touchscreen in which the target sometimes changed during the movement (“interrupted reach”). Article: subjects were off anti-Parkinsonian medication; one or two Neuroprobe Sonus tungsten microelectrodes (1 MOhm, Alpha Omega), 2 mm apart, driven on one microdrive; recordings referenced to the outer cannula; raw signals sampled at 44 kHz. ## Source - Dryad: Dennis London, Arash Fazl, Kalman Katlowitz, Marisol Soula, Michael Pourfar, Alon Mogilner, Roozbeh Kiani. Distinct population code for movement kinematics and changes of ongoing movements in human

subthalamic nucleus. doi:10.5061/dryad.2jm63xsq2. License CC0 1.0.

Article: eLife 10:e64893 (2021), doi:10.7554/eLife.64893. Code: https://github.com/dlondon12/InterruptedReachClustMass
The 8 subject zips were downloaded through the Dryad API on Voyager and matched the Dryad SHA-256 digests. Not mirrored: CWT_mean.zip, Hilbert_beta_mean.zip, PPC_*.zip (time-frequency and phase-consistency derivatives) and processed_data.mat (spiking/behaviour summary); they remain on Dryad.

## Contents - sub-<nn>/ieeg/sub-<nn>_task-interruptedreach_run-<block>_ieeg.*: 16 task blocks of 8 subjects

(2.57 h). run = the release block number. Channels: CRAW_xx = microelectrode tip, CMacro_RAW_xx = macro contact of the same lead, wideband RAW streams at the acquisition rate (44 kHz; Sub04: 22 kHz, rate from the stored sample count and begin/end times). Values: int16 x BitResolution / Gain (both stored per channel) in µV, float32.

events.tsv: digital port words (CPORT__1) and digital-input rising edges (CDIG_IN_x) on the RAW time axis; see task-interruptedreach_events.json for the time convention. Trial-level behaviour (touch trajectories, trial types) is in the behaviour .mat files in sourcedata.
sourcedata/dryad-2jm63xsq2/SubXX/BlockYY/: every file of the subject zips: the complete Alpha Omega export (also the online LFP streams at 1.375 kHz, spike segments, digital channels), the behaviour/task .mat and the Plexon spike-time .txt files (sorted units). Changes: MAT-file header dates set to day 01; file names with patient initials and a date were renamed (4 files: Sub02_OR_HandR_STNL_Block02.mat, Sub09_HandR_STNL_Block02.mat, plexon_Sub02_OR_HandR_STNL_Block02_spikes1.txt, plexon_Sub02_OR_HandR_STNL_Block02_spikes2.txt).

## Timing caveat Sampling rate = the nominal rate stored per channel (_KHz). In some exports the stored begin/end times span more time than samples / rate, which indicates recording pauses concatenated in the export; event onsets there are exact only up to the first pause: Sub02/Block01: 29792526 samples over TimeBegin-TimeEnd 1634.2 s (18231.1 Hz vs nominal 44000 Hz); Sub04/Block01: 8873514 samples over TimeBegin-TimeEnd 172.2 s (51522.8 Hz vs nominal 22000 Hz); Sub11/Block01: 29672592 samples over TimeBegin-TimeEnd 133.8 s (221785.2 Hz vs nominal 44000 Hz); Sub11/Block05: 26669440 samples over TimeBegin-TimeEnd 139.9 s (190625.3 Hz vs nominal 44000 Hz); Sub11/Block06: 28558928 samples over TimeBegin-TimeEnd 138.0 s (207009.3 Hz vs nominal 44000 Hz); Sub11/Block07: 27740369 samples over TimeBegin-TimeEnd 135.2 s (205178.1 Hz vs nominal 44000 Hz); Sub13/Block02: 30603712 samples over TimeBegin-TimeEnd 139.1 s (219961.2 Hz vs nominal 44000 Hz). ## Localisation Recordings were from the STN (article); the side comes from the file names (STNL/STNR). No coordinates or depths are in the release; electrodes.tsv lists the channels with x, y, z = n/a. ## Participants Age/sex per patient are in the article’s Supplementary file 1, which is not linked to subject numbers in the release; n/a here. ## Privacy File names with initials and dates were renamed; MAT headers keep year and month only. String scan of the behaviour .mat files: no names, dates or identifiers found.

License: CC0-1.0

Authors:

Dennis London
Arash Fazl
Kalman Katlowitz
Marisol Soula
Michael Pourfar
… and 2 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000429

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

245

Sampling frequencies (Hz)

2200044000

Total recording duration: 2 h 34 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 4 (10), 2 (5), 5 ch · iEEG · 44000 Hz · mixed · 8 subjects, 16 recordings

Live trace viewer — sub-01 · task-interruptedreach · run-02

Showing one representative recording out of 8 subjects and 16 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.NM000429(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000429

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

class eegdash.dataset.NM000429(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021)

Study:: nm000429 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000429.

Modality: ieeg; Subject type: Unknown. Subjects: 8; recordings: 16; 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/nm000429 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000429 DOI: https://doi.org/10.82901/nemar.nm000429

Examples

>>> from eegdash.dataset import NM000429
>>> dataset = NM000429(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 — NM000429.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 nm000429 to reproduce the tutorial on this dataset.

Citation

Dennis London, Arash Fazl, Kalman Katlowitz, Marisol Soula, Michael Pourfar, … (2021). Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021). 10.82901/nemar.nm000429

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000429.

Related & sibling datasets

NM000477IEEG · 24 subj NM000436IEEG · 9 subj NM000464IEEG · 80 subj NM000461IEEG · 150 subj NM000433IEEG · 1 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · eeg.json

Provenance

CC0-1.0 · 10.82901/nemar.nm000429

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000429`
Title	Human subthalamic nucleus microelectrode recordings during an interrupted reach task (London et al., 2021)
Author (year)	—
Canonical	—
Importable as	`NM000429`
Year	2021
Authors	Dennis London, Arash Fazl, Kalman Katlowitz, Marisol Soula, Michael Pourfar, Alon Mogilner, Roozbeh Kiani
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000429
Source links	OpenNeuro \| NeMAR \| Source URL

NM000429: ieeg dataset, 8 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#