ON007990: fnirs dataset, 63 subjects#
Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy
Access recordings and metadata through EEGDash.
Citation: Gopika Satish, Leah M. Taylor, Megan P. Arnold, Julia Gallagher-Shale, Karthikeyan Krishnamurthy, Gregory J. Basura (—). Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy. 10.82901/nemar.on007990
Modality: fnirs Subjects: 63 Recordings: 63 License: CC0 Source: nemar
Metadata: Complete (100%)
63-participant fNIRS dataset — Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON007990
dataset = ON007990(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON007990(cache_dir="./data", subject="01")
Advanced query
dataset = ON007990(
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{on007990,
title = {Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy},
author = {Gopika Satish and Leah M. Taylor and Megan P. Arnold and Julia Gallagher-Shale and Karthikeyan Krishnamurthy and Gregory J. Basura},
doi = {10.82901/nemar.on007990},
url = {https://doi.org/10.82901/nemar.on007990},
}
About This Dataset#
Functional near-infrared spectroscopy (fNIRS) recordings of human auditory cortex in participants with subjective tinnitus and controls, acquired with a NIRx NIRSport2 system.
Groups are recorded in participants.tsv:
control -> participant IDs sub-0xx experimental -> participant IDs sub-1xx
Tinnitus fNIRS auditory cortex study
The original_label column maps each participant to the source folder.
Cohort#
Dataset Statistics#
Age distribution (n=63, range 26–79 yr, mean 56.5 yr · sex per subject not reported)
Sex composition
Channel counts: 166 ch (n=63 recordings)
Sampling frequencies: 5.0864699898270604 Hz (n=63 recordings)
Total recording duration: 17 h 34 min
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 |
Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Gopika Satish, Leah M. Taylor, Megan P. Arnold, Julia Gallagher-Shale, Karthikeyan Krishnamurthy, Gregory J. Basura |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on007990,
title = {Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy},
author = {Gopika Satish and Leah M. Taylor and Megan P. Arnold and Julia Gallagher-Shale and Karthikeyan Krishnamurthy and Gregory J. Basura},
doi = {10.82901/nemar.on007990},
url = {https://doi.org/10.82901/nemar.on007990},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON007990(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy
- Study:
on007990(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON007990.Modality:
fnirs; Subject type:Unknown. Subjects: 63; recordings: 63; 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/on007990 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007990 DOI: https://doi.org/10.82901/nemar.on007990
Examples
>>> from eegdash.dataset import ON007990 >>> dataset = ON007990(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.pytorch
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 on007990 to reproduce the tutorial on this dataset.
Citation
Gopika Satish, Leah M. Taylor, Megan P. Arnold, Julia Gallagher-Shale, Karthikeyan Krishnamurthy, … (n.d.). Subjective tinnitus severity correlates with objective changes in human auditory cortex as measured by functional near-infrared spectroscopy. 10.82901/nemar.on007990
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on007990.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset