DS006902: fnirs dataset, 42 subjects#
Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy
Citation: Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär (—). Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy. 10.18112/openneuro.ds006902.v1.1.1
42-participant fNIRS dataset — Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006902
dataset = DS006902(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006902(cache_dir="./data", subject="01")
Advanced query
dataset = DS006902(
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{ds006902,
title = {Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy},
author = {Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär},
doi = {10.18112/openneuro.ds006902.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds006902.v1.1.1},
}
About This Dataset#
Regular physical activity is an important treatment constituent for chronic pain. To unravel the neuronal influence of exercise on pain, we investigated the neuronal changes during exercise-induced hypoalgesia in endurance athletes and controls.
Twenty-two athletes (mean age: 33.3 ± 10.8 years) and twenty non-athletes (mean age: 28.9 ± 9.0 years) underwent High-Intensity Interval Training (HIIT) and pressure pain tests, while brain oxygenation was monitored using functional near-infrared spectroscopy to cover key regions of pain processing: the prefrontal cortex (PFC), sensory motor cortices, and posterior parietal cortex (PPC).
During HIIT, both groups exhibited a steady increase in PFC oxyhemoglobin, with athletes showing a greater increase in the PPC area than non-athletes. As expected, athletes showed a significant reduction in pain perception after HIIT, whereas non-athletes did not. In line, athletes showed a significant decrease in oxyhemoglobin levels in all brain areas post-HIIT, while non-athletes only showed a decrease in sensory motor areas. Interestingly, in athletes, pain reduction correlated with the decrease in PFC oxyhemoglobin during painful stimulation, whereas no significant correlation was observed in non-athletes.
The pronounced HIIT-induced increase in oxyhemoglobin in athletes may elevate baseline neural activity to a level where additional activation is limited, potentially reducing the salience of pain-related signals. This athlete-specific response may result from endurance training adaptations, such as enhanced microvascularization and oxygen delivery, promoting greater neural efficiency during high-intensity exercise. These findings highlight HIIT’s potential as a targeted pain management strategy for athletes and the need for tailored approaches in non-athletes. dataset: sub01-sub27 are athletes; sub29-sub53 are non-athletes
Cohort#
Dataset Statistics#
Channel counts: 112 ch (n=42 recordings)
Sampling frequencies: 7.627765064836003 Hz (n=42 recordings)
Total recording duration: 27 h
Signal · Electrodes & live trace#
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006902,
title = {Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy},
author = {Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär},
doi = {10.18112/openneuro.ds006902.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds006902.v1.1.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006902 · Geisler2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006902(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy
- Study:
ds006902(OpenNeuro)- Author (year):
Geisler2025- Canonical:
—
Also importable as:
DS006902,Geisler2025.Modality:
fnirs; Experiment type:Perception; Subject type:Healthy. Subjects: 42; recordings: 42; tasks: 1.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds006902 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006902 DOI: https://doi.org/10.18112/openneuro.ds006902.v1.1.1
Examples
>>> from eegdash.dataset import DS006902 >>> dataset = DS006902(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds006902").huggingface
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 ds006902 to reproduce the tutorial on this dataset.
Citation
Maria Geisler, Marco Herbsleb, Feliberto de la Cruz, Sabrina von Au, Andy Schumann, Ilona Croy, Karl-Jürgen Bär (n.d.). Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy. 10.18112/openneuro.ds006902.v1.1.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006902.v1.1.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset