EEGdash›NeMAR›NM000434
Iss. 434 · 11 subjects · 88 recordings · CC-BY-4.0
Dataset Brief · SenseFOG

NM000434: eeg, ieeg dataset, 11 subjects#

SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson’s disease

Access recordings and metadata through EEGDash.

Citation: Philipp Klocke, Moritz Loeffler, Daniel Weiss (2024). SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson’s disease. 10.82901/nemar.nm000434

Modality: eeg, ieeg Subjects: 11 Recordings: 88 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

11-participant EEG, iEEG dataset — SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson's disease.

Data & curation Philipp Klocke · Moritz Loeffler · Daniel Weiss
Senior author Daniel Weiss
Year 2024 · Distributed via NeMAR
EEG, iEEG · 58 (45), 2 (43) ch250, 1000 HzBIDS 1.10.05 tasks6 sessions
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 NM000434

dataset = NM000434(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000434(cache_dir="./data", subject="01")

Advanced query

dataset = NM000434(
    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{nm000434,
  title = {SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson's disease},
  author = {Philipp Klocke and Moritz Loeffler and Daniel Weiss},
  doi = {10.82901/nemar.nm000434},
  url = {https://doi.org/10.82901/nemar.nm000434},
}
§ 02Study · The README

About This Dataset#

Patients with idiopathic Parkinson’s disease and bilateral subthalamic (STN) DBS with a Medtronic Percept PC

implantable pulse generator, recorded at the University Hospital Tübingen (Department of Neurology) while sitting, standing, walking through a narrow corridor, walking with self-initiated stops and walking with a dual task (serial subtraction), after overnight withdrawal of dopaminergic medication and with stimulation OFF (release documentation). Signals: bipolar STN local field potentials streamed by the Percept (BrainSense, 250 Hz), 48-channel scalp EEG with 8 surface EMG channels and 2 ‘IPG’ channels (Brain Products, 1000 Hz), and three APDM Opal inertial sensors (lumbar spine and both legs/feet, 128 Hz).

All 277 files were downloaded and matched the SHA-256 checksums listed by Mendeley.

DOI

SenseFOG: STN LFP, EEG/EMG and IMU during gait and freezing of gait in Parkinson’s disease

  • Documentation and analysis code: PKlocke/SenseFOG-Documentation (study design, inclusion criteria, task descriptions and the authors’ hard-coded walking/stop/freezing time points live there).

  • Ethics: Ethics Committee of Tübingen University (166/2020BO1).

Contents

View full README

DOI

SenseFOG: STN LFP, EEG/EMG and IMU during gait and freezing of gait in Parkinson’s disease

  • Documentation and analysis code: PKlocke/SenseFOG-Documentation (study design, inclusion criteria, task descriptions and the authors’ hard-coded walking/stop/freezing time points live there).

  • Ethics: Ethics Committee of Tübingen University (166/2020BO1).

Contents

  • 12 participants (sub-XX = release subject id).

  • ieeg/: 43 Percept BrainSense TimeDomain recordings (2.50 h), 2 bipolar channels each, named with the Percept stream label (e.g. ONE_THREE_LEFT = contact levels 1 and 3 of the left lead). Values are the Percept TimeDomainData unchanged (float32 BrainVision); Percept JSON exports store these in µV. Missing packets are not filled in; the Percept packet sequence numbers are in the sourcedata JSON. No contact coordinates are released: electrodes.tsv lists the contact levels with x/y/z = n/a, the lead model and the stated target (STN).

  • eeg/: 45 BrainVision recordings (2.73 h). The .eeg files are byte-identical to the release; the headers were rewritten only to point at the BIDS file names (the released headers named the pre-rename files, e.g. PD01_gehen.eeg). Channel types: EEG, EMG (TA_/GA_), MISC (IPG_1/IPG_2). events.tsv lists the recorded markers.

  • motion/: 43 APDM Opal recordings: the ‘Calibrated’ accelerometer, gyroscope, magnetometer, orientation and temperature arrays of each sensor, written exactly (17 significant digits). Tracked points use the sensor labels stored in each file (Lumbar, Left/Right Leg or Foot).

  • Task labels: the release’s ses-<condition> folders are conditions of one visit and are BIDS tasks here: sitting, standing, walk, walkws (walking with stops), walkint (dual task; sub-14 has two: run-1, run-2).

  • Not all modalities exist for every condition. Not converted (kept in sourcedata): the authors’ pre-aligned files *_lfpalg.mat, *_eegalg.mat, *_gaitalg.mat (sub-10 all conditions, ECG-artefact-cleaned; sub-05 standing; sub-11 and sub-17 sitting LFP), the gait-event files *_gaitfilt_cor.mat, and the Percept export of sub-11 sitting (no time-domain stream). EEG, LFP and IMU are not resampled or aligned to each other here; the release aligns them on the stimulation artefact at the start of each recording.

Privacy

  • Percept exports: every date (session, device clock, implant, date of birth, event logs, packet times) set to day 01 of its month (year, month and time of day kept); free-text therapy group names removed. Patient name fields hold only placeholders in the release (e.g. ‘NAME, FIRST’, ‘MUSTERMANN, MAX’); serial numbers are masked.

  • BrainVision marker files: ‘New Segment’ recording date set to day 01. APDM HDF5 copies: the absolute time vectors (Time, SyncValue, Annotations) shifted back by whole days so they fall on day 01 of the month; sensor data unchanged.

  • MATLAB files: dates embedded in the authors’ structures and in the MAT header set to day 01; numeric contents verified equal.

  • BIDS files carry no dates. No scans.tsv acquisition times.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000434-blue)](https://doi.org/10.82901/nemar.nm000434) # SenseFOG: STN LFP, EEG/EMG and IMU during gait and freezing of gait in Parkinson’s disease Patients with idiopathic Parkinson’s disease and bilateral subthalamic (STN) DBS with a Medtronic Percept PC implantable pulse generator, recorded at the University Hospital Tübingen (Department of Neurology) while sitting, standing, walking through a narrow corridor, walking with self-initiated stops and walking with a dual task (serial subtraction), after overnight withdrawal of dopaminergic medication and with stimulation OFF (release documentation). Signals: bipolar STN local field potentials streamed by the Percept (BrainSense, 250 Hz), 48-channel scalp EEG with 8 surface EMG channels and 2 ‘IPG’ channels (Brain Products, 1000 Hz), and three APDM Opal inertial sensors (lumbar spine and both legs/feet, 128 Hz). ## Source - Philipp Klocke, Moritz Loeffler, Daniel Weiss (2024). “Supraspinal circuits contribute to the failure of antagonistic inhibition and freezing of gait in Parkinson’s disease”. Mendeley Data, V1, doi:10.17632/c9ckcvjxc7.1. Licence CC BY 4.0.

All 277 files were downloaded and matched the SHA-256 checksums listed by Mendeley.

  • Documentation and analysis code: PKlocke/SenseFOG-Documentation (study design, inclusion criteria, task descriptions and the authors’ hard-coded walking/stop/freezing time points live there).

  • Ethics: Ethics Committee of Tübingen University (166/2020BO1).

## Contents - 12 participants (sub-XX = release subject id). - ieeg/: 43 Percept BrainSense TimeDomain recordings (2.50 h), 2 bipolar channels each, named

with the Percept stream label (e.g. ONE_THREE_LEFT = contact levels 1 and 3 of the left lead). Values are the Percept TimeDomainData unchanged (float32 BrainVision); Percept JSON exports store these in µV. Missing packets are not filled in; the Percept packet sequence numbers are in the sourcedata JSON. No contact coordinates are released: electrodes.tsv lists the contact levels with x/y/z = n/a, the lead model and the stated target (STN).

  • eeg/: 45 BrainVision recordings (2.73 h). The .eeg files are byte-identical to the release; the headers were rewritten only to point at the BIDS file names (the released headers named the pre-rename files, e.g. PD01_gehen.eeg). Channel types: EEG, EMG (TA_/GA_), MISC (IPG_1/IPG_2). events.tsv lists the recorded markers.

  • motion/: 43 APDM Opal recordings: the ‘Calibrated’ accelerometer, gyroscope, magnetometer, orientation and temperature arrays of each sensor, written exactly (17 significant digits). Tracked points use the sensor labels stored in each file (Lumbar, Left/Right Leg or Foot).

  • Task labels: the release’s ses-<condition> folders are conditions of one visit and are BIDS tasks here: sitting, standing, walk, walkws (walking with stops), walkint (dual task; sub-14 has two: run-1, run-2).

  • Not all modalities exist for every condition. Not converted (kept in sourcedata): the authors’ pre-aligned files *_lfpalg.mat, *_eegalg.mat, *_gaitalg.mat (sub-10 all conditions, ECG-artefact-cleaned; sub-05 standing; sub-11 and sub-17 sitting LFP), the gait-event files *_gaitfilt_cor.mat, and the Percept export of sub-11 sitting (no time-domain stream). EEG, LFP and IMU are not resampled or aligned to each other here; the release aligns them on the stimulation artefact at the start of each recording.

## Privacy - Percept exports: every date (session, device clock, implant, date of birth, event logs, packet times) set to day

01 of its month (year, month and time of day kept); free-text therapy group names removed. Patient name fields hold only placeholders in the release (e.g. ‘NAME, FIRST’, ‘MUSTERMANN, MAX’); serial numbers are masked.

  • BrainVision marker files: ‘New Segment’ recording date set to day 01. APDM HDF5 copies: the absolute time vectors (Time, SyncValue, Annotations) shifted back by whole days so they fall on day 01 of the month; sensor data unchanged.

  • MATLAB files: dates embedded in the authors’ structures and in the MAT header set to day 01; numeric contents verified equal.

  • BIDS files carry no dates. No scans.tsv acquisition times.

License: CC-BY-4.0

Authors:

  • Philipp Klocke

  • Moritz Loeffler

  • Daniel Weiss

Versions:

Version

DOI

Released

current

10.82901/nemar.nm000434
§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sex composition

4
subjects
Female
2
Male
2
F : M ratio
1.00 : 1
50% female · n = 4 subjects with reported sex.

Channel counts (ch)

258

Sampling frequencies (Hz)

2501000

Total recording duration: 5 h 13 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 58 (45), 2 (43) ch · EEG, iEEG · 250, 1000 Hz · 11 subjects, 88 recordings
Live trace viewer — sub-01 · task-walkws

Showing one representative recording out of 11 subjects and 88 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 48 sensors — 48 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.

NEMAR pipeline statistics — NM000434
HED event descriptors word cloud HED event descriptors word cloud — NM000434
§ 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

NM000434

Title

SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson’s disease

Author (year)

—

Canonical

—

Importable as

NM000434

Year

2024

Authors

Philipp Klocke, Moritz Loeffler, Daniel Weiss

License

CC-BY-4.0

Citation / DOI

10.82901/nemar.nm000434

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000434,
  title = {SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson's disease},
  author = {Philipp Klocke and Moritz Loeffler and Daniel Weiss},
  doi = {10.82901/nemar.nm000434},
  url = {https://doi.org/10.82901/nemar.nm000434},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000434(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asNM000434
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000434(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson’s disease

Study:

nm000434 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: NM000434.

Modality: eeg, ieeg; Subject type: Unknown. Subjects: 11; recordings: 88; tasks: 5.

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/nm000434 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000434 DOI: https://doi.org/10.82901/nemar.nm000434

Examples

>>> from eegdash.dataset import NM000434
>>> dataset = NM000434(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 — NM000434.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 nm000434 to reproduce the tutorial on this dataset.

Citation

Philipp Klocke, Moritz Loeffler, Daniel Weiss (2024). SenseFOG: subthalamic LFP (Medtronic Percept), EEG/EMG and IMU kinematics during sitting, standing, walking, stopping and freezing of gait in Parkinson's disease. 10.82901/nemar.nm000434

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000434.

BIDS
BIDS 1.10.0
Sidecars
events · channels · eeg.json
Provenance
CC-BY-4.0 · 10.82901/nemar.nm000434
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#