EEGdash›NeMAR›NM000465
Iss. 465 · 23 subjects · 47 recordings · CC0-1.0
Dataset Brief · Intraoperative STN/thalamic DBS-lead LFP with subgaleal senso…

NM000465: ieeg dataset, 23 subjects#

Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021)

Access recordings and metadata through EEGDash.

Citation: Darcy A. Diesburg, Jeremy D. W. Greenlee, Jan R. Wessel (2021). Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021). 10.82901/nemar.nm000465

Modality: ieeg Subjects: 23 Recordings: 47 License: CC0-1.0 Source: nemar

Metadata: Complete (100%)

23-participant iEEG dataset — Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021).

Data & curation Darcy A. Diesburg · Jeremy D. W. Greenlee · Jan R. Wessel
Senior author Jan R. Wessel
Year 2021 · Distributed via NeMAR
Funding NIH R01NS117753 (J.R.W.) · NIH T32GM108540 (D.A.D.) · + 3 more
iEEG · 20 (18), 17 (13), 24 (10), 21 (3), 25 (2), 4 ch2000 Hz · mixedBIDS 1.10.02 tasks
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000465

dataset = NM000465(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000465(cache_dir="./data", subject="01")

Advanced query

dataset = NM000465(
    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{nm000465,
  title = {Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021)},
  author = {Darcy A. Diesburg and Jeremy D. W. Greenlee and Jan R. Wessel},
  doi = {10.82901/nemar.nm000465},
  url = {https://doi.org/10.82901/nemar.nm000465},
}
§ 02Study · The README

About This Dataset#

Local field potentials recorded during deep brain stimulation surgery (University of Iowa Hospitals and Clinics, April 2018 -

March 2020; DataREADME) from DBS leads (Medtronic 3387) in the subthalamic nucleus (STN) or the ventral intermediate thalamus (VIM), together with 4-, 6- or 8-contact strips in the subgaleal space over sensorimotor cortex, while patients performed an auditory stop-signal task (“main”) and a go-only localizer. Article: beta bursts in STN/thalamus on successful stop trials are followed within 50 ms by beta bursts over sensorimotor cortex.

doi:10.5061/dryad.gf1vhhmq0. License CC0 1.0. Article: eLife 10:e70270 (2021), doi:10.7554/eLife.70270.

DOI

Intraoperative STN/thalamic LFP + subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021)

  • Authors’ code (channel tables getchans.m/getlocchans.m): Zenodo doi:10.5281/zenodo.4784092 (CC-BY-4.0), copied unchanged to sourcedata/zenodo-4784092.

  • data.zip, behav.zip and DataREADME.txt were downloaded through the Dryad API on Voyager (SHA-256 verified). Not mirrored: pp.zip (EEGLAB preprocessed data) and beta.zip (beta-burst indices), both derived.

View full README

DOI

Intraoperative STN/thalamic LFP + subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021)

  • Authors’ code (channel tables getchans.m/getlocchans.m): Zenodo doi:10.5281/zenodo.4784092 (CC-BY-4.0), copied unchanged to sourcedata/zenodo-4784092.

  • data.zip, behav.zip and DataREADME.txt were downloaded through the Dryad API on Voyager (SHA-256 verified). Not mirrored: pp.zip (EEGLAB preprocessed data) and beta.zip (beta-burst indices), both derived.

Contents

  • sub-<id>/ieeg/sub-<id>_task-stopsignal_* and task-localizer: 47 recordings, 23 subjects, 7.33 h. Sampling rates 2000, 24000 Hz (TDT export, per file). Values unchanged from the release (float32); units are not stated (n/a). Dryad methods: TDT system with RA16PA Medusa preamplifier and RA16LI headstage, hardware low-pass 7.5 kHz.

  • Channels: LFPx_RZ2_chnNNN = recording channels, labelled from the authors’ tables (DBS = STN/VIM lead contacts 0-3; EEG = subgaleal strip contacts, which lie under the scalp outside the skull); Inpt_RZ2_chnNNN = analog TTL inputs (TRIG) that carry the task triggers (DataREADME: “Triggers are represented as up-flanks (from TTL pulses) in an input channel”). Channels without a label in the tables: Subject_116_1/main:LFPx_RZ2_chn004, Subject_116_1/main:LFPx_RZ2_chn013, Subject_116_1/main:LFPx_RZ2_chn014, Subject_116_1/main:LFPx_RZ2_chn015, Subject_116_1/main:LFPx_RZ2_chn016, Subject_117/main:LFPx_RZ2_chn020, Subject_118/main:LFPx_RZ2_chn020, Subject_119/main:LFPx_RZ2_chn017, Subject_119/main:LFPx_RZ2_chn018, Subject_119/main:LFPx_RZ2_chn019, Subject_119/main:LFPx_RZ2_chn020, Subject_122/main:LFPx_RZ2_chn017, Subject_122/main:LFPx_RZ2_chn018, Subject_122/main:LFPx_RZ2_chn019, Subject_122/main:LFPx_RZ2_chn020, Subject_126/main:LFPx_RZ2_chn017, Subject_126/main:LFPx_RZ2_chn018, Subject_126/main:LFPx_RZ2_chn019, Subject_126/main:LFPx_RZ2_chn020, Subject_128/main:LFPx_RZ2_chn001, Subject_128/main:LFPx_RZ2_chn002, Subject_128/main:LFPx_RZ2_chn003, Subject_128/main:LFPx_RZ2_chn004, Subject_128/main:LFPx_RZ2_chn005, Subject_128/main:LFPx_RZ2_chn006, Subject_128/main:LFPx_RZ2_chn007, Subject_128/main:LFPx_RZ2_chn008, Subject_128/main:LFPx_RZ2_chn009, Subject_128/main:LFPx_RZ2_chn010, Subject_128/main:LFPx_RZ2_chn011, Subject_128/main:LFPx_RZ2_chn012, Subject_128/main:LFPx_RZ2_chn013, Subject_128/main:LFPx_RZ2_chn014, Subject_128/main:LFPx_RZ2_chn015, Subject_128/main:LFPx_RZ2_chn016, Subject_128/main:LFPx_RZ2_chn017, Subject_128/main:LFPx_RZ2_chn018, Subject_128/main:LFPx_RZ2_chn019, Subject_128/main:LFPx_RZ2_chn020, Subject_129/main:LFPx_RZ2_chn017, Subject_129/main:LFPx_RZ2_chn018, Subject_129/main:LFPx_RZ2_chn019, Subject_129/main:LFPx_RZ2_chn020, Subject_101/localizers:LFPx_RZ2_chn009, Subject_101/localizers:LFPx_RZ2_chn010, Subject_101/localizers:LFPx_RZ2_chn011, Subject_101/localizers:LFPx_RZ2_chn012, Subject_101/localizers:LFPx_RZ2_chn013, Subject_101/localizers:LFPx_RZ2_chn014, Subject_101/localizers:LFPx_RZ2_chn015, Subject_101/localizers:LFPx_RZ2_chn016, Subject_104/localizers:LFPx_RZ2_chn005, Subject_104/localizers:LFPx_RZ2_chn006, Subject_104/localizers:LFPx_RZ2_chn007, Subject_104/localizers:LFPx_RZ2_chn008, Subject_104/localizers:LFPx_RZ2_chn009, Subject_104/localizers:LFPx_RZ2_chn010, Subject_104/localizers:LFPx_RZ2_chn011, Subject_104/localizers:LFPx_RZ2_chn012, Subject_104/localizers:LFPx_RZ2_chn013, Subject_104/localizers:LFPx_RZ2_chn014, Subject_104/localizers:LFPx_RZ2_chn015, Subject_104/localizers:LFPx_RZ2_chn016, Subject_105/localizers:LFPx_RZ2_chn009, Subject_105/localizers:LFPx_RZ2_chn010, Subject_105/localizers:LFPx_RZ2_chn011, Subject_105/localizers:LFPx_RZ2_chn012, Subject_105/localizers:LFPx_RZ2_chn013, Subject_105/localizers:LFPx_RZ2_chn014, Subject_105/localizers:LFPx_RZ2_chn015, Subject_105/localizers:LFPx_RZ2_chn016, Subject_106/localizers:LFPx_RZ2_chn009, Subject_106/localizers:LFPx_RZ2_chn010, Subject_106/localizers:LFPx_RZ2_chn011, Subject_106/localizers:LFPx_RZ2_chn012, Subject_106/localizers:LFPx_RZ2_chn013, Subject_106/localizers:LFPx_RZ2_chn014, Subject_106/localizers:LFPx_RZ2_chn015, Subject_106/localizers:LFPx_RZ2_chn016, Subject_109/localizers:LFPx_RZ2_chn009, Subject_109/localizers:LFPx_RZ2_chn010, Subject_109/localizers:LFPx_RZ2_chn011, Subject_109/localizers:LFPx_RZ2_chn012, Subject_109/localizers:LFPx_RZ2_chn013, Subject_109/localizers:LFPx_RZ2_chn014, Subject_109/localizers:LFPx_RZ2_chn015, Subject_109/localizers:LFPx_RZ2_chn016, Subject_110/localizers:LFPx_RZ2_chn009, Subject_110/localizers:LFPx_RZ2_chn010, Subject_110/localizers:LFPx_RZ2_chn011, Subject_110/localizers:LFPx_RZ2_chn012, Subject_110/localizers:LFPx_RZ2_chn013, Subject_110/localizers:LFPx_RZ2_chn014, Subject_110/localizers:LFPx_RZ2_chn015, Subject_110/localizers:LFPx_RZ2_chn016, Subject_113/localizers:LFPx_RZ2_chn013, Subject_113/localizers:LFPx_RZ2_chn014, Subject_113/localizers:LFPx_RZ2_chn015, Subject_113/localizers:LFPx_RZ2_chn016, Subject_114/localizers:LFPx_RZ2_chn013, Subject_114/localizers:LFPx_RZ2_chn014, Subject_114/localizers:LFPx_RZ2_chn015, Subject_114/localizers:LFPx_RZ2_chn016, Subject_114/localizers:LFPx_RZ2_chn017, Subject_114/localizers:LFPx_RZ2_chn018, Subject_114/localizers:LFPx_RZ2_chn019, Subject_114/localizers:LFPx_RZ2_chn020, Subject_115/localizers:LFPx_RZ2_chn017, Subject_115/localizers:LFPx_RZ2_chn018, Subject_115/localizers:LFPx_RZ2_chn019, Subject_115/localizers:LFPx_RZ2_chn020, Subject_115/localizers:LFPx_RZ2_chn021, Subject_115/localizers:LFPx_RZ2_chn022, Subject_115/localizers:LFPx_RZ2_chn023, Subject_115/localizers:LFPx_RZ2_chn024, Subject_117/localizers:LFPx_RZ2_chn009, Subject_117/localizers:LFPx_RZ2_chn010, Subject_117/localizers:LFPx_RZ2_chn011, Subject_117/localizers:LFPx_RZ2_chn012, Subject_117/localizers:LFPx_RZ2_chn013, Subject_117/localizers:LFPx_RZ2_chn014, Subject_117/localizers:LFPx_RZ2_chn015, Subject_117/localizers:LFPx_RZ2_chn016, Subject_117/localizers:LFPx_RZ2_chn017, Subject_117/localizers:LFPx_RZ2_chn018, Subject_117/localizers:LFPx_RZ2_chn019, Subject_117/localizers:LFPx_RZ2_chn020, Subject_118/localizers:LFPx_RZ2_chn009, Subject_118/localizers:LFPx_RZ2_chn010, Subject_118/localizers:LFPx_RZ2_chn011, Subject_118/localizers:LFPx_RZ2_chn012, Subject_118/localizers:LFPx_RZ2_chn013, Subject_118/localizers:LFPx_RZ2_chn014, Subject_118/localizers:LFPx_RZ2_chn015, Subject_118/localizers:LFPx_RZ2_chn016, Subject_118/localizers:LFPx_RZ2_chn017, Subject_118/localizers:LFPx_RZ2_chn018, Subject_118/localizers:LFPx_RZ2_chn019, Subject_118/localizers:LFPx_RZ2_chn020, Subject_119/localizers:LFPx_RZ2_chn009, Subject_119/localizers:LFPx_RZ2_chn010, Subject_119/localizers:LFPx_RZ2_chn011, Subject_119/localizers:LFPx_RZ2_chn012, Subject_119/localizers:LFPx_RZ2_chn013, Subject_119/localizers:LFPx_RZ2_chn014, Subject_119/localizers:LFPx_RZ2_chn015, Subject_119/localizers:LFPx_RZ2_chn016, Subject_119/localizers:LFPx_RZ2_chn017, Subject_119/localizers:LFPx_RZ2_chn018, Subject_119/localizers:LFPx_RZ2_chn019, Subject_119/localizers:LFPx_RZ2_chn020, Subject_120/localizers:LFPx_RZ2_chn009, Subject_120/localizers:LFPx_RZ2_chn010, Subject_120/localizers:LFPx_RZ2_chn011, Subject_120/localizers:LFPx_RZ2_chn012, Subject_120/localizers:LFPx_RZ2_chn013, Subject_120/localizers:LFPx_RZ2_chn014, Subject_120/localizers:LFPx_RZ2_chn015, Subject_120/localizers:LFPx_RZ2_chn016, Subject_121/localizers:LFPx_RZ2_chn009, Subject_121/localizers:LFPx_RZ2_chn010, Subject_121/localizers:LFPx_RZ2_chn011, Subject_121/localizers:LFPx_RZ2_chn012, Subject_121/localizers:LFPx_RZ2_chn013, Subject_121/localizers:LFPx_RZ2_chn014, Subject_121/localizers:LFPx_RZ2_chn015, Subject_121/localizers:LFPx_RZ2_chn016, Subject_122/localizers:LFPx_RZ2_chn009, Subject_122/localizers:LFPx_RZ2_chn010, Subject_122/localizers:LFPx_RZ2_chn011, Subject_122/localizers:LFPx_RZ2_chn012, Subject_122/localizers:LFPx_RZ2_chn013, Subject_122/localizers:LFPx_RZ2_chn014, Subject_122/localizers:LFPx_RZ2_chn015, Subject_122/localizers:LFPx_RZ2_chn016, Subject_122/localizers:LFPx_RZ2_chn017, Subject_122/localizers:LFPx_RZ2_chn018, Subject_122/localizers:LFPx_RZ2_chn019, Subject_122/localizers:LFPx_RZ2_chn020, Subject_125/localizers:LFPx_RZ2_chn009, Subject_125/localizers:LFPx_RZ2_chn010, Subject_125/localizers:LFPx_RZ2_chn011, Subject_125/localizers:LFPx_RZ2_chn012, Subject_125/localizers:LFPx_RZ2_chn013, Subject_125/localizers:LFPx_RZ2_chn014, Subject_125/localizers:LFPx_RZ2_chn015, Subject_125/localizers:LFPx_RZ2_chn016, Subject_126/localizers:LFPx_RZ2_chn009, Subject_126/localizers:LFPx_RZ2_chn010, Subject_126/localizers:LFPx_RZ2_chn011, Subject_126/localizers:LFPx_RZ2_chn012, Subject_126/localizers:LFPx_RZ2_chn013, Subject_126/localizers:LFPx_RZ2_chn014, Subject_126/localizers:LFPx_RZ2_chn015, Subject_126/localizers:LFPx_RZ2_chn016, Subject_126/localizers:LFPx_RZ2_chn017, Subject_126/localizers:LFPx_RZ2_chn018, Subject_126/localizers:LFPx_RZ2_chn019, Subject_126/localizers:LFPx_RZ2_chn020, Subject_128/localizers:LFPx_RZ2_chn009, Subject_128/localizers:LFPx_RZ2_chn010, Subject_128/localizers:LFPx_RZ2_chn011, Subject_128/localizers:LFPx_RZ2_chn012, Subject_128/localizers:LFPx_RZ2_chn013, Subject_128/localizers:LFPx_RZ2_chn014, Subject_128/localizers:LFPx_RZ2_chn015, Subject_128/localizers:LFPx_RZ2_chn016, Subject_128/localizers:LFPx_RZ2_chn017, Subject_128/localizers:LFPx_RZ2_chn018, Subject_128/localizers:LFPx_RZ2_chn019, Subject_128/localizers:LFPx_RZ2_chn020, Subject_129/localizers:LFPx_RZ2_chn009, Subject_129/localizers:LFPx_RZ2_chn010, Subject_129/localizers:LFPx_RZ2_chn011, Subject_129/localizers:LFPx_RZ2_chn012, Subject_129/localizers:LFPx_RZ2_chn013, Subject_129/localizers:LFPx_RZ2_chn014, Subject_129/localizers:LFPx_RZ2_chn015, Subject_129/localizers:LFPx_RZ2_chn016, Subject_129/localizers:LFPx_RZ2_chn017, Subject_129/localizers:LFPx_RZ2_chn018, Subject_129/localizers:LFPx_RZ2_chn019, Subject_129/localizers:LFPx_RZ2_chn020, Subject_130/localizers:LFPx_RZ2_chn009, Subject_130/localizers:LFPx_RZ2_chn010, Subject_130/localizers:LFPx_RZ2_chn011, Subject_130/localizers:LFPx_RZ2_chn012, Subject_130/localizers:LFPx_RZ2_chn013, Subject_130/localizers:LFPx_RZ2_chn014, Subject_130/localizers:LFPx_RZ2_chn015, Subject_130/localizers:LFPx_RZ2_chn016.

  • events.tsv: TDT epoc stores (tick markers etc.). Trial-level behaviour (stop-signal delays, responses) is in sourcedata/dryad-gf1vhhmq0/behav/.

Localisation

Per-contact structure and side from the authors’ channel tables; no coordinates are released.

Privacy

The TDT tank names in the original files contain a recording date; the copies in sourcedata/dryad-gf1vhhmq0/data were re-saved with the day set to 01 (MAT headers too). The behaviour files contain no age/sex/name (empty fields); output folder paths only.

Ethics

Quoted verbatim from the article (Diesburg DA, Greenlee JDW, Wessel JR (2021), eLife 10:e70270, doi:10.7554/eLife.70270; section “Additional information” > “Ethics”, full text Europe PMC PMC8691838):

Human subjects: Research participants signed a written informed consent document during a clinic visit prior to surgery. Experimental protocols were approved by the University of Iowa’s Institutional Review Board (#201402720).

The Materials and methods (“Participants”) state the same approval: “These experimental protocols were approved by the University of Iowa’s Institutional Review Board (#201402720).” The Dryad deposit doi:10.5061/dryad.gf1vhhmq0 is the data release of this article.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000465-blue)](https://doi.org/10.82901/nemar.nm000465) # Intraoperative STN/thalamic LFP + subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021) Local field potentials recorded during deep brain stimulation surgery (University of Iowa Hospitals and Clinics, April 2018 - March 2020; DataREADME) from DBS leads (Medtronic 3387) in the subthalamic nucleus (STN) or the ventral intermediate thalamus (VIM), together with 4-, 6- or 8-contact strips in the subgaleal space over sensorimotor cortex, while patients performed an auditory stop-signal task (“main”) and a go-only localizer. Article: beta bursts in STN/thalamus on successful stop trials are followed within 50 ms by beta bursts over sensorimotor cortex. ## Source - Dryad: Darcy A. Diesburg, Jeremy D. W. Greenlee, Jan R. Wessel. Cortico-subcortical beta burst dynamics underlying movement cancellation in humans.

doi:10.5061/dryad.gf1vhhmq0. License CC0 1.0. Article: eLife 10:e70270 (2021), doi:10.7554/eLife.70270.

  • Authors’ code (channel tables getchans.m/getlocchans.m): Zenodo doi:10.5281/zenodo.4784092 (CC-BY-4.0), copied unchanged to sourcedata/zenodo-4784092.

  • data.zip, behav.zip and DataREADME.txt were downloaded through the Dryad API on Voyager (SHA-256 verified). Not mirrored: pp.zip (EEGLAB preprocessed data) and beta.zip (beta-burst indices), both derived.

## Contents - sub-<id>/ieeg/sub-<id>_task-stopsignal_* and task-localizer: 47 recordings, 23 subjects, 7.33 h.

Sampling rates 2000, 24000 Hz (TDT export, per file). Values unchanged from the release (float32); units are not stated (n/a). Dryad methods: TDT system with RA16PA Medusa preamplifier and RA16LI headstage, hardware low-pass 7.5 kHz.

  • Channels: LFPx_RZ2_chnNNN = recording channels, labelled from the authors’ tables (DBS = STN/VIM lead contacts 0-3; EEG = subgaleal strip contacts, which lie under the scalp outside the skull); Inpt_RZ2_chnNNN = analog TTL inputs (TRIG) that carry the task triggers (DataREADME: “Triggers are represented as up-flanks (from TTL pulses) in an input channel”). Channels without a label in the tables: Subject_116_1/main:LFPx_RZ2_chn004, Subject_116_1/main:LFPx_RZ2_chn013, Subject_116_1/main:LFPx_RZ2_chn014, Subject_116_1/main:LFPx_RZ2_chn015, Subject_116_1/main:LFPx_RZ2_chn016, Subject_117/main:LFPx_RZ2_chn020, Subject_118/main:LFPx_RZ2_chn020, Subject_119/main:LFPx_RZ2_chn017, Subject_119/main:LFPx_RZ2_chn018, Subject_119/main:LFPx_RZ2_chn019, Subject_119/main:LFPx_RZ2_chn020, Subject_122/main:LFPx_RZ2_chn017, Subject_122/main:LFPx_RZ2_chn018, Subject_122/main:LFPx_RZ2_chn019, Subject_122/main:LFPx_RZ2_chn020, Subject_126/main:LFPx_RZ2_chn017, Subject_126/main:LFPx_RZ2_chn018, Subject_126/main:LFPx_RZ2_chn019, Subject_126/main:LFPx_RZ2_chn020, Subject_128/main:LFPx_RZ2_chn001, Subject_128/main:LFPx_RZ2_chn002, Subject_128/main:LFPx_RZ2_chn003, Subject_128/main:LFPx_RZ2_chn004, Subject_128/main:LFPx_RZ2_chn005, Subject_128/main:LFPx_RZ2_chn006, Subject_128/main:LFPx_RZ2_chn007, Subject_128/main:LFPx_RZ2_chn008, Subject_128/main:LFPx_RZ2_chn009, Subject_128/main:LFPx_RZ2_chn010, Subject_128/main:LFPx_RZ2_chn011, Subject_128/main:LFPx_RZ2_chn012, Subject_128/main:LFPx_RZ2_chn013, Subject_128/main:LFPx_RZ2_chn014, Subject_128/main:LFPx_RZ2_chn015, Subject_128/main:LFPx_RZ2_chn016, Subject_128/main:LFPx_RZ2_chn017, Subject_128/main:LFPx_RZ2_chn018, Subject_128/main:LFPx_RZ2_chn019, Subject_128/main:LFPx_RZ2_chn020, Subject_129/main:LFPx_RZ2_chn017, Subject_129/main:LFPx_RZ2_chn018, Subject_129/main:LFPx_RZ2_chn019, Subject_129/main:LFPx_RZ2_chn020, Subject_101/localizers:LFPx_RZ2_chn009, Subject_101/localizers:LFPx_RZ2_chn010, Subject_101/localizers:LFPx_RZ2_chn011, Subject_101/localizers:LFPx_RZ2_chn012, Subject_101/localizers:LFPx_RZ2_chn013, Subject_101/localizers:LFPx_RZ2_chn014, Subject_101/localizers:LFPx_RZ2_chn015, Subject_101/localizers:LFPx_RZ2_chn016, Subject_104/localizers:LFPx_RZ2_chn005, Subject_104/localizers:LFPx_RZ2_chn006, Subject_104/localizers:LFPx_RZ2_chn007, Subject_104/localizers:LFPx_RZ2_chn008, Subject_104/localizers:LFPx_RZ2_chn009, Subject_104/localizers:LFPx_RZ2_chn010, Subject_104/localizers:LFPx_RZ2_chn011, Subject_104/localizers:LFPx_RZ2_chn012, Subject_104/localizers:LFPx_RZ2_chn013, Subject_104/localizers:LFPx_RZ2_chn014, Subject_104/localizers:LFPx_RZ2_chn015, Subject_104/localizers:LFPx_RZ2_chn016, Subject_105/localizers:LFPx_RZ2_chn009, Subject_105/localizers:LFPx_RZ2_chn010, Subject_105/localizers:LFPx_RZ2_chn011, Subject_105/localizers:LFPx_RZ2_chn012, Subject_105/localizers:LFPx_RZ2_chn013, Subject_105/localizers:LFPx_RZ2_chn014, Subject_105/localizers:LFPx_RZ2_chn015, Subject_105/localizers:LFPx_RZ2_chn016, Subject_106/localizers:LFPx_RZ2_chn009, Subject_106/localizers:LFPx_RZ2_chn010, Subject_106/localizers:LFPx_RZ2_chn011, Subject_106/localizers:LFPx_RZ2_chn012, Subject_106/localizers:LFPx_RZ2_chn013, Subject_106/localizers:LFPx_RZ2_chn014, Subject_106/localizers:LFPx_RZ2_chn015, Subject_106/localizers:LFPx_RZ2_chn016, Subject_109/localizers:LFPx_RZ2_chn009, Subject_109/localizers:LFPx_RZ2_chn010, Subject_109/localizers:LFPx_RZ2_chn011, Subject_109/localizers:LFPx_RZ2_chn012, Subject_109/localizers:LFPx_RZ2_chn013, Subject_109/localizers:LFPx_RZ2_chn014, Subject_109/localizers:LFPx_RZ2_chn015, Subject_109/localizers:LFPx_RZ2_chn016, Subject_110/localizers:LFPx_RZ2_chn009, Subject_110/localizers:LFPx_RZ2_chn010, Subject_110/localizers:LFPx_RZ2_chn011, Subject_110/localizers:LFPx_RZ2_chn012, Subject_110/localizers:LFPx_RZ2_chn013, Subject_110/localizers:LFPx_RZ2_chn014, Subject_110/localizers:LFPx_RZ2_chn015, Subject_110/localizers:LFPx_RZ2_chn016, Subject_113/localizers:LFPx_RZ2_chn013, Subject_113/localizers:LFPx_RZ2_chn014, Subject_113/localizers:LFPx_RZ2_chn015, Subject_113/localizers:LFPx_RZ2_chn016, Subject_114/localizers:LFPx_RZ2_chn013, Subject_114/localizers:LFPx_RZ2_chn014, Subject_114/localizers:LFPx_RZ2_chn015, Subject_114/localizers:LFPx_RZ2_chn016, Subject_114/localizers:LFPx_RZ2_chn017, Subject_114/localizers:LFPx_RZ2_chn018, Subject_114/localizers:LFPx_RZ2_chn019, Subject_114/localizers:LFPx_RZ2_chn020, Subject_115/localizers:LFPx_RZ2_chn017, Subject_115/localizers:LFPx_RZ2_chn018, Subject_115/localizers:LFPx_RZ2_chn019, Subject_115/localizers:LFPx_RZ2_chn020, Subject_115/localizers:LFPx_RZ2_chn021, Subject_115/localizers:LFPx_RZ2_chn022, Subject_115/localizers:LFPx_RZ2_chn023, Subject_115/localizers:LFPx_RZ2_chn024, Subject_117/localizers:LFPx_RZ2_chn009, Subject_117/localizers:LFPx_RZ2_chn010, Subject_117/localizers:LFPx_RZ2_chn011, Subject_117/localizers:LFPx_RZ2_chn012, Subject_117/localizers:LFPx_RZ2_chn013, Subject_117/localizers:LFPx_RZ2_chn014, Subject_117/localizers:LFPx_RZ2_chn015, Subject_117/localizers:LFPx_RZ2_chn016, Subject_117/localizers:LFPx_RZ2_chn017, Subject_117/localizers:LFPx_RZ2_chn018, Subject_117/localizers:LFPx_RZ2_chn019, Subject_117/localizers:LFPx_RZ2_chn020, Subject_118/localizers:LFPx_RZ2_chn009, Subject_118/localizers:LFPx_RZ2_chn010, Subject_118/localizers:LFPx_RZ2_chn011, Subject_118/localizers:LFPx_RZ2_chn012, Subject_118/localizers:LFPx_RZ2_chn013, Subject_118/localizers:LFPx_RZ2_chn014, Subject_118/localizers:LFPx_RZ2_chn015, Subject_118/localizers:LFPx_RZ2_chn016, Subject_118/localizers:LFPx_RZ2_chn017, Subject_118/localizers:LFPx_RZ2_chn018, Subject_118/localizers:LFPx_RZ2_chn019, Subject_118/localizers:LFPx_RZ2_chn020, Subject_119/localizers:LFPx_RZ2_chn009, Subject_119/localizers:LFPx_RZ2_chn010, Subject_119/localizers:LFPx_RZ2_chn011, Subject_119/localizers:LFPx_RZ2_chn012, Subject_119/localizers:LFPx_RZ2_chn013, Subject_119/localizers:LFPx_RZ2_chn014, Subject_119/localizers:LFPx_RZ2_chn015, Subject_119/localizers:LFPx_RZ2_chn016, Subject_119/localizers:LFPx_RZ2_chn017, Subject_119/localizers:LFPx_RZ2_chn018, Subject_119/localizers:LFPx_RZ2_chn019, Subject_119/localizers:LFPx_RZ2_chn020, Subject_120/localizers:LFPx_RZ2_chn009, Subject_120/localizers:LFPx_RZ2_chn010, Subject_120/localizers:LFPx_RZ2_chn011, Subject_120/localizers:LFPx_RZ2_chn012, Subject_120/localizers:LFPx_RZ2_chn013, Subject_120/localizers:LFPx_RZ2_chn014, Subject_120/localizers:LFPx_RZ2_chn015, Subject_120/localizers:LFPx_RZ2_chn016, Subject_121/localizers:LFPx_RZ2_chn009, Subject_121/localizers:LFPx_RZ2_chn010, Subject_121/localizers:LFPx_RZ2_chn011, Subject_121/localizers:LFPx_RZ2_chn012, Subject_121/localizers:LFPx_RZ2_chn013, Subject_121/localizers:LFPx_RZ2_chn014, Subject_121/localizers:LFPx_RZ2_chn015, Subject_121/localizers:LFPx_RZ2_chn016, Subject_122/localizers:LFPx_RZ2_chn009, Subject_122/localizers:LFPx_RZ2_chn010, Subject_122/localizers:LFPx_RZ2_chn011, Subject_122/localizers:LFPx_RZ2_chn012, Subject_122/localizers:LFPx_RZ2_chn013, Subject_122/localizers:LFPx_RZ2_chn014, Subject_122/localizers:LFPx_RZ2_chn015, Subject_122/localizers:LFPx_RZ2_chn016, Subject_122/localizers:LFPx_RZ2_chn017, Subject_122/localizers:LFPx_RZ2_chn018, Subject_122/localizers:LFPx_RZ2_chn019, Subject_122/localizers:LFPx_RZ2_chn020, Subject_125/localizers:LFPx_RZ2_chn009, Subject_125/localizers:LFPx_RZ2_chn010, Subject_125/localizers:LFPx_RZ2_chn011, Subject_125/localizers:LFPx_RZ2_chn012, Subject_125/localizers:LFPx_RZ2_chn013, Subject_125/localizers:LFPx_RZ2_chn014, Subject_125/localizers:LFPx_RZ2_chn015, Subject_125/localizers:LFPx_RZ2_chn016, Subject_126/localizers:LFPx_RZ2_chn009, Subject_126/localizers:LFPx_RZ2_chn010, Subject_126/localizers:LFPx_RZ2_chn011, Subject_126/localizers:LFPx_RZ2_chn012, Subject_126/localizers:LFPx_RZ2_chn013, Subject_126/localizers:LFPx_RZ2_chn014, Subject_126/localizers:LFPx_RZ2_chn015, Subject_126/localizers:LFPx_RZ2_chn016, Subject_126/localizers:LFPx_RZ2_chn017, Subject_126/localizers:LFPx_RZ2_chn018, Subject_126/localizers:LFPx_RZ2_chn019, Subject_126/localizers:LFPx_RZ2_chn020, Subject_128/localizers:LFPx_RZ2_chn009, Subject_128/localizers:LFPx_RZ2_chn010, Subject_128/localizers:LFPx_RZ2_chn011, Subject_128/localizers:LFPx_RZ2_chn012, Subject_128/localizers:LFPx_RZ2_chn013, Subject_128/localizers:LFPx_RZ2_chn014, Subject_128/localizers:LFPx_RZ2_chn015, Subject_128/localizers:LFPx_RZ2_chn016, Subject_128/localizers:LFPx_RZ2_chn017, Subject_128/localizers:LFPx_RZ2_chn018, Subject_128/localizers:LFPx_RZ2_chn019, Subject_128/localizers:LFPx_RZ2_chn020, Subject_129/localizers:LFPx_RZ2_chn009, Subject_129/localizers:LFPx_RZ2_chn010, Subject_129/localizers:LFPx_RZ2_chn011, Subject_129/localizers:LFPx_RZ2_chn012, Subject_129/localizers:LFPx_RZ2_chn013, Subject_129/localizers:LFPx_RZ2_chn014, Subject_129/localizers:LFPx_RZ2_chn015, Subject_129/localizers:LFPx_RZ2_chn016, Subject_129/localizers:LFPx_RZ2_chn017, Subject_129/localizers:LFPx_RZ2_chn018, Subject_129/localizers:LFPx_RZ2_chn019, Subject_129/localizers:LFPx_RZ2_chn020, Subject_130/localizers:LFPx_RZ2_chn009, Subject_130/localizers:LFPx_RZ2_chn010, Subject_130/localizers:LFPx_RZ2_chn011, Subject_130/localizers:LFPx_RZ2_chn012, Subject_130/localizers:LFPx_RZ2_chn013, Subject_130/localizers:LFPx_RZ2_chn014, Subject_130/localizers:LFPx_RZ2_chn015, Subject_130/localizers:LFPx_RZ2_chn016.

  • events.tsv: TDT epoc stores (tick markers etc.). Trial-level behaviour (stop-signal delays, responses) is in sourcedata/dryad-gf1vhhmq0/behav/.

## Localisation Per-contact structure and side from the authors’ channel tables; no coordinates are released. ## Privacy The TDT tank names in the original files contain a recording date; the copies in sourcedata/dryad-gf1vhhmq0/data were re-saved with the day set to 01 (MAT headers too). The behaviour files contain no age/sex/name (empty fields); output folder paths only. ## Ethics Quoted verbatim from the article (Diesburg DA, Greenlee JDW, Wessel JR (2021), eLife 10:e70270, doi:10.7554/eLife.70270; section “Additional information” > “Ethics”, full text Europe PMC PMC8691838): > Human subjects: Research participants signed a written informed consent document during a clinic visit prior to surgery. Experimental protocols were approved by the University of Iowa’s Institutional Review Board (#201402720). The Materials and methods (“Participants”) state the same approval: “These experimental protocols were approved by the University of Iowa’s Institutional Review Board (#201402720).” The Dryad deposit doi:10.5061/dryad.gf1vhhmq0 is the data release of this article.

License: CC0-1.0

Authors:

  • Darcy A. Diesburg

  • Jeremy D. W. Greenlee

  • Jan R. Wessel

Versions:

Version

DOI

Released

current

10.82901/nemar.nm000465
§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

41720212425

Sampling frequencies (Hz)

200024000

Total recording duration: 7 h 20 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 20 (18), 17 (13), 24 (10), 21 (3), 25 (2), 4 ch · iEEG · 2000 Hz · mixed · 23 subjects, 47 recordings
Live trace viewer — sub-130 · task-localizer

Showing one representative recording out of 23 subjects and 47 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.

NEMAR pipeline statistics — NM000465
HED event descriptors word cloud HED event descriptors word cloud — NM000465
§ 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…
Full dataset metadata table

Dataset ID

NM000465

Title

Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021)

Author (year)

—

Canonical

—

Importable as

NM000465

Year

2021

Authors

Darcy A. Diesburg, Jeremy D. W. Greenlee, Jan R. Wessel

License

CC0-1.0

Citation / DOI

10.82901/nemar.nm000465

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000465,
  title = {Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021)},
  author = {Darcy A. Diesburg and Jeremy D. W. Greenlee and Jan R. Wessel},
  doi = {10.82901/nemar.nm000465},
  url = {https://doi.org/10.82901/nemar.nm000465},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000465(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asNM000465
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000465(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021)

Study:

nm000465 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: NM000465.

Modality: ieeg; Subject type: Unknown. Subjects: 23; recordings: 47; tasks: 2.

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/nm000465 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000465 DOI: https://doi.org/10.82901/nemar.nm000465

Examples

>>> from eegdash.dataset import NM000465
>>> dataset = NM000465(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 — NM000465.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 nm000465 to reproduce the tutorial on this dataset.

Citation

Darcy A. Diesburg, Jeremy D. W. Greenlee, Jan R. Wessel (2021). Intraoperative STN/thalamic DBS-lead LFP with subgaleal sensorimotor strips during a stop-signal task (Diesburg, Greenlee & Wessel, 2021). 10.82901/nemar.nm000465

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000465.

BIDS
BIDS 1.10.0
Sidecars
events · channels · eeg.json
Provenance
CC0-1.0 · 10.82901/nemar.nm000465
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#