EEGdash›NeMAR›NM000436

Iss. 436 · 9 subjects · 84 recordings · CC0-1.0

Dataset Brief · Intraoperative VIM thalamus microelectrode recordings during…

NM000436: ieeg dataset, 9 subjects#

Intraoperative VIM thalamus microelectrode recordings during transcutaneous afferent patterned stimulation (TAPS) for essential tremor (Luu et al.; derivative: authors’ processed 48 kHz signals)

Access recordings and metadata through EEGDash.

Citation: Cuong P. Luu, Jordan Ranum, Youngwon Youn, Jennifer L. Perrault, Bryan M. Krause, Matthew I. Banks, Laura Buyan-Dent, Kip A. Ludwig, Wendell B. Lake, Aaron J. Suminski (2025). Intraoperative VIM thalamus microelectrode recordings during transcutaneous afferent patterned stimulation (TAPS) for essential tremor (Luu et al.; derivative: authors’ processed 48 kHz signals). 10.82901/nemar.nm000436

Modality: ieeg Subjects: 9 Recordings: 84 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

9-participant iEEG dataset — Intraoperative VIM thalamus microelectrode recordings during transcutaneous afferent patterned stimulation (TAPS) for essential tremor (Luu et al.; derivative: authors' processed 48 kHz signals).

Data & curation Cuong P. Luu · Jordan Ranum · Youngwon Youn · Jennifer L. Perrault · Bryan M. Krause · Matthew I. Banks · …
Senior author Aaron J. Suminski
Year 2025 · Distributed via NeMAR
Funding University of Wisconsin-Madison School of Medicine and Public Health · Partial funding of AJS and WBL from Cala Health, Inc. (preprint)

iEEG · 3 ch48000 HzBIDS 1.10.0Task · taps

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 NM000436

dataset = NM000436(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 = NM000436(cache_dir="./data", subject="01")

Advanced query

dataset = NM000436(
    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{nm000436,
  title = {Intraoperative VIM thalamus microelectrode recordings during transcutaneous afferent patterned stimulation (TAPS) for essential tremor (Luu et al.; derivative: authors' processed 48 kHz signals)},
  author = {Cuong P. Luu and Jordan Ranum and Youngwon Youn and Jennifer L. Perrault and Bryan M. Krause and Matthew I. Banks and Laura Buyan-Dent and Kip A. Ludwig and Wendell B. Lake and Aaron J. Suminski},
  doi = {10.82901/nemar.nm000436},
  url = {https://doi.org/10.82901/nemar.nm000436},
}

§ 02Study · The README

About This Dataset#

Intraoperative microelectrode recordings (MER) from the ventral intermediate nucleus of the thalamus (VIM) region of

9 adults with medication-refractory essential tremor undergoing DBS surgery (University of Wisconsin-Madison, IRB 2018-1052).

At each recording depth, 60 s were recorded without and with transcutaneous afferent patterned stimulation (TAPS) of the

treated hand, a wearable peripheral nerve stimulation therapy. Remifentanil was stopped 15 min before mapping (article).

DOI

VIM thalamus microelectrode recordings during TAPS for essential tremor (Luu et al. 2025) - DERIVATIVE

THIS IS A DERIVATIVE DATASET: the original recordings were never released

The original FHC recordings (.apm files) and the authors’ intermediate ‘R files’ (output of MER_loadData.m) are NOT part of the Dryad release. The release contains only the authors’ processed MATLAB files (‘P-’ = “Processed neural data after signal processing and artifact removal”, Dryad README) and derived files.

View full README

DOI

VIM thalamus microelectrode recordings during TAPS for essential tremor (Luu et al. 2025) - DERIVATIVE

THIS IS A DERIVATIVE DATASET: the original recordings were never released

The original FHC recordings (.apm files) and the authors’ intermediate ‘R files’ (output of MER_loadData.m) are NOT part of the Dryad release. The release contains only the authors’ processed MATLAB files (‘P-’ = “Processed neural data after signal processing and artifact removal”, Dryad README) and derived files.
The signals here are the P-File field raw exactly as released (cast to float32): the authors’ software-filtered, offset-removed (zero-mean), 60 s-trimmed signals. Evidence (measured on all files in Voyager Jobs ieeg-b3w10-vim-insp-1008005934 and ieeg-b3w10-vim-quant-1008010513): - every trace is exactly 2,880,000 samples = 60.0 s at 48 kHz per trial, while the acquisition timestamps (trialTimes)

span 2,880,013-2,880,061 ticks: trimmed to exactly 60 s;
- the mean of each 60 s trace is ~1e-5..6e-4 µV against a standard deviation of 1e3..7e3 µV (relative 1e-8): offset removed in software;
- the power spectrum falls by 13-20 orders of magnitude between 6-8 kHz and 20-24 kHz, i.e. a steep double-precision digital low-pass near 8-10 kHz, whereas the article states a 20 kHz hardware band limit;
- the values are not on any ADC quantisation grid (FHC .apm files store integer samples times voltageCalib), so they are not a scaled copy of the acquired samples.
Further derived fields of the P-Files (lfp, spk, rawLPCheby, rawBPCheby, raw_blanked, spk_blanked, spike waveforms, PSDs) are not converted; they remain available in sourcedata/P-Files.

Contents

9 participants (sub-<NN> = study subject number 02, 03, 06-12 of IRB study CH1052), 84 runs, 1.40 h at 48 kHz.
task-taps_acq-off = P-File base (TAPS off); task-taps_acq-on = P-File stim (TAPS on). Runs are numbered in chronological order of acquisition within each subject (P-File trialTimes). A few P-File cells hold two consecutive 60 s trials; each trial is a run, placed right after the cell’s first trial (trialTimes gives only the first trial’s times, so later trials have n/a trial ticks in scans.tsv).
sub-<NN>_scans.tsv: TAPS state, depth label and depth (mm above target), source P-File cell, acquisition-clock trial times, TAPS frequency/amplitude, fraction of samples in the authors’ artefact mask.
Channels Ant, Cen, Pos: the anterior, center and posterior Ben-gun microelectrodes (2 mm apart), each recorded bipolar against the macro contact 1 cm dorsal to its tip (article); type DBS, µV (unit from the authors’ figure labels).
events.tsv: the whole trial (taps_off_trial / taps_on_trial) and the segments of the authors’ motion-artefact blanking mask (artifact_blanked_by_authors). The data samples themselves are not blanked.
Data format: BrainVision IEEE float32. BrainVision has no float64; the float64 P-File values are stored as float32 (relative rounding <= 6e-8, at most 0.002 µV, far below the ~0.022 µV ADC step). Every file was read back and is bit-identical to float32 of the P-File values. The float64 originals are in sourcedata/.
Electrodes: space-Other per subject, with the P-File coordinates in mm (chanCoord for each track and depth, implantCoord and therapyCoord for the final DBS lead and therapeutic contact). CAVEAT: the authors do not state the frame. The values (x 0, y +2/0/-2 for the anterior/center/posterior tracks, z = depth in mm above the planned target) look like offsets from the planned VIM target along the trajectory. They are NOT MNI/Talairach coordinates; do not use them for atlas lookups.
participants.tsv: age and sex are n/a (the article reports only group values: 65 +/- 7 years, 2 of 9 female); implant side (P-File), TAPS-treated hand (TETRAS log), TAPS settings (P-File).
Empty P-File cells (no recording) are skipped; they are listed in code/conversion_report.json.
sourcedata/: P-Files (MAT header creation date set to day 01, HDF5 content unchanged), the Dryad README, and the root folder zip (analysis scripts, TETRAS scores, stimVbaseSPK.mat; member and MAT header dates set to day 01).

Source

Dryad: Cuong P. Luu, Jordan Ranum, Youngwon Youn, Jennifer L. Perrault, Bryan M. Krause, Matthew I. Banks, Laura Buyan-Dent, Kip A. Ludwig, Wendell B. Lake, Aaron J. Suminski. “Real-time VIM thalamus recordings during peripheral nerve stimulation treatment for essential tremor: DBS intraoperative dataset”. doi:10.5061/dryad.cjsxksngp (CC0-1.0).
Article: Luu et al. (2025) “Wearable peripheral nerve stimulator reduces essential tremor symptoms through targeted brain modulation”, Brain Stimulation, doi:10.1016/j.brs.2025.06.004. Preprint doi:10.1101/2024.12.02.24317799.
Funding: University of Wisconsin-Madison School of Medicine and Public Health; AJS and WBL were partly funded by Cala Health, Inc. (preprint).

Privacy

The release contains study codes only (CH1052-NN). Strings in the P-Files are depth labels and implant side. MAT header file-creation dates (June 2024, processing dates) were set to day 01. No names or recording dates were found.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000436-blue)](https://doi.org/10.82901/nemar.nm000436) # VIM thalamus microelectrode recordings during TAPS for essential tremor (Luu et al. 2025) - DERIVATIVE Intraoperative microelectrode recordings (MER) from the ventral intermediate nucleus of the thalamus (VIM) region of 9 adults with medication-refractory essential tremor undergoing DBS surgery (University of Wisconsin-Madison, IRB 2018-1052). At each recording depth, 60 s were recorded without and with transcutaneous afferent patterned stimulation (TAPS) of the treated hand, a wearable peripheral nerve stimulation therapy. Remifentanil was stopped 15 min before mapping (article). ## THIS IS A DERIVATIVE DATASET: the original recordings were never released - The original FHC recordings (.apm files) and the authors’ intermediate ‘R files’ (output of MER_loadData.m) are NOT part

of the Dryad release. The release contains only the authors’ processed MATLAB files (‘P-’ = “Processed neural data after signal processing and artifact removal”, Dryad README) and derived files.

The signals here are the P-File field raw exactly as released (cast to float32): the authors’ software-filtered, offset-removed (zero-mean), 60 s-trimmed signals. Evidence (measured on all files in Voyager Jobs ieeg-b3w10-vim-insp-1008005934 and ieeg-b3w10-vim-quant-1008010513): - every trace is exactly 2,880,000 samples = 60.0 s at 48 kHz per trial, while the acquisition timestamps (trialTimes)

span 2,880,013-2,880,061 ticks: trimmed to exactly 60 s;
- the mean of each 60 s trace is ~1e-5..6e-4 µV against a standard deviation of 1e3..7e3 µV (relative 1e-8): offset removed in software;
- the power spectrum falls by 13-20 orders of magnitude between 6-8 kHz and 20-24 kHz, i.e. a steep double-precision digital low-pass near 8-10 kHz, whereas the article states a 20 kHz hardware band limit;
- the values are not on any ADC quantisation grid (FHC .apm files store integer samples times voltageCalib), so they are not a scaled copy of the acquired samples.
Further derived fields of the P-Files (lfp, spk, rawLPCheby, rawBPCheby, raw_blanked, spk_blanked, spike waveforms, PSDs) are not converted; they remain available in sourcedata/P-Files.

## Contents - 9 participants (sub-<NN> = study subject number 02, 03, 06-12 of IRB study CH1052), 84 runs, 1.40 h at 48 kHz. - task-taps_acq-off = P-File base (TAPS off); task-taps_acq-on = P-File stim (TAPS on). Runs are numbered in

chronological order of acquisition within each subject (P-File trialTimes). A few P-File cells hold two consecutive 60 s trials; each trial is a run, placed right after the cell’s first trial (trialTimes gives only the first trial’s times, so later trials have n/a trial ticks in scans.tsv).

sub-<NN>_scans.tsv: TAPS state, depth label and depth (mm above target), source P-File cell, acquisition-clock trial times, TAPS frequency/amplitude, fraction of samples in the authors’ artefact mask.
Channels Ant, Cen, Pos: the anterior, center and posterior Ben-gun microelectrodes (2 mm apart), each recorded bipolar against the macro contact 1 cm dorsal to its tip (article); type DBS, µV (unit from the authors’ figure labels).
events.tsv: the whole trial (taps_off_trial / taps_on_trial) and the segments of the authors’ motion-artefact blanking mask (artifact_blanked_by_authors). The data samples themselves are not blanked.
Data format: BrainVision IEEE float32. BrainVision has no float64; the float64 P-File values are stored as float32 (relative rounding <= 6e-8, at most 0.002 µV, far below the ~0.022 µV ADC step). Every file was read back and is bit-identical to float32 of the P-File values. The float64 originals are in sourcedata/.
Electrodes: space-Other per subject, with the P-File coordinates in mm (chanCoord for each track and depth, implantCoord and therapyCoord for the final DBS lead and therapeutic contact). CAVEAT: the authors do not state the frame. The values (x 0, y +2/0/-2 for the anterior/center/posterior tracks, z = depth in mm above the planned target) look like offsets from the planned VIM target along the trajectory. They are NOT MNI/Talairach coordinates; do not use them for atlas lookups.
participants.tsv: age and sex are n/a (the article reports only group values: 65 +/- 7 years, 2 of 9 female); implant side (P-File), TAPS-treated hand (TETRAS log), TAPS settings (P-File).
Empty P-File cells (no recording) are skipped; they are listed in code/conversion_report.json.
sourcedata/: P-Files (MAT header creation date set to day 01, HDF5 content unchanged), the Dryad README, and the root folder zip (analysis scripts, TETRAS scores, stimVbaseSPK.mat; member and MAT header dates set to day 01).

## Source - Dryad: Cuong P. Luu, Jordan Ranum, Youngwon Youn, Jennifer L. Perrault, Bryan M. Krause, Matthew I. Banks, Laura Buyan-Dent, Kip A. Ludwig, Wendell B. Lake, Aaron J. Suminski. “Real-time VIM thalamus recordings during peripheral nerve stimulation treatment for essential

tremor: DBS intraoperative dataset”. doi:10.5061/dryad.cjsxksngp (CC0-1.0).

Article: Luu et al. (2025) “Wearable peripheral nerve stimulator reduces essential tremor symptoms through targeted brain modulation”, Brain Stimulation, doi:10.1016/j.brs.2025.06.004. Preprint doi:10.1101/2024.12.02.24317799.
Funding: University of Wisconsin-Madison School of Medicine and Public Health; AJS and WBL were partly funded by Cala Health, Inc. (preprint).

## Privacy The release contains study codes only (CH1052-NN). Strings in the P-Files are depth labels and implant side. MAT header file-creation dates (June 2024, processing dates) were set to day 01. No names or recording dates were found.

License: CC0-1.0

Authors:

Cuong P. Luu
Jordan Ranum
Youngwon Youn
Jennifer L. Perrault
Bryan M. Krause
… and 5 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000436

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 3 ch (n=84 recordings)

Sampling frequencies: 48000.0 Hz (n=84 recordings)

Total recording duration: 1 h 24 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 3 ch · iEEG · 48000 Hz · 9 subjects, 84 recordings

Live trace viewer — sub-02 · task-taps · run-09

Showing one representative recording out of 9 subjects and 84 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.

Electrode layout — iEEG · 20 sensors — 20 channels

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.NM000436(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000436

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

class eegdash.dataset.NM000436(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Intraoperative VIM thalamus microelectrode recordings during transcutaneous afferent patterned stimulation (TAPS) for essential tremor (Luu et al.; derivative: authors’ processed 48 kHz signals)

Study:: nm000436 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000436.

Modality: ieeg; Subject type: Unknown. Subjects: 9; recordings: 84; 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/nm000436 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000436 DOI: https://doi.org/10.82901/nemar.nm000436

Examples

>>> from eegdash.dataset import NM000436
>>> dataset = NM000436(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 — NM000436.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 nm000436 to reproduce the tutorial on this dataset.

Citation

Cuong P. Luu, Jordan Ranum, Youngwon Youn, Jennifer L. Perrault, Bryan M. Krause, … (2025). Intraoperative VIM thalamus microelectrode recordings during transcutaneous afferent patterned stimulation (TAPS) for essential tremor (Luu et al.; derivative: authors' processed 48 kHz signals). 10.82901/nemar.nm000436

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000436.

Related & sibling datasets

NM000429IEEG · 8 subj NM000477IEEG · 24 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.nm000436

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000436`
Title	Intraoperative VIM thalamus microelectrode recordings during transcutaneous afferent patterned stimulation (TAPS) for essential tremor (Luu et al.; derivative: authors’ processed 48 kHz signals)
Author (year)	—
Canonical	—
Importable as	`NM000436`
Year	2025
Authors	Cuong P. Luu, Jordan Ranum, Youngwon Youn, Jennifer L. Perrault, Bryan M. Krause, Matthew I. Banks, Laura Buyan-Dent, Kip A. Ludwig, Wendell B. Lake, Aaron J. Suminski
License	CC0-1.0
Citation / DOI	10.82901/nemar.nm000436
Source links	OpenNeuro \| NeMAR \| Source URL

NM000436: ieeg dataset, 9 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#