DS004563: eeg dataset, 40 subjects#
Vicarious touch: overlapping neural patterns between seeing and feeling touch
Citation: Sophie Smit, Denise Moerel, Regine Zopf, Anina N Rich (—). Vicarious touch: overlapping neural patterns between seeing and feeling touch. 10.18112/openneuro.ds004563.v1.0.1
40-participant EEG dataset — Vicarious touch: overlapping neural patterns between seeing and feeling touch.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004563
dataset = DS004563(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004563(cache_dir="./data", subject="01")
Advanced query
dataset = DS004563(
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{ds004563,
title = {Vicarious touch: overlapping neural patterns between seeing and feeling touch},
author = {Sophie Smit and Denise Moerel and Regine Zopf and Anina N Rich},
doi = {10.18112/openneuro.ds004563.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004563.v1.0.1},
}
About This Dataset#
Data collection took place at Macquarie University in Sydney Australia. The study was approved by the Macquarie University Ethics Committee.
We used time-resolved multivariate pattern analysis on whole-brain EEG data from people with and without vicarious touch experiences to test whether seen touch evokes overlapping neural representations with the first-hand experience of touch. Participants felt touch to the fingers (tactile trials) or watched carefully matched videos of touch to another person’s fingers (visual trials).
There were 12 runs in total, divided into four blocks of 36 trials (with alternating sets of nine tactile and nine visual trials) resulting in a total of 1728 trials (864 tactile and 864 visual). There were an additional 240 target trials (20 per run), which were excluded from analysis.
Between trials there was an inter-trial-interval of 800ms. Each run lasted approximately 7-8 minutes with short breaks between blocks and runs. Whole brain 64-channel EEG data were recorded using an Active Two Biosemi system (Biosemi, Inc.) at 2048Hz and 10-20 standard caps. Stimuli were presented using MATLAB (MathWorks) and Psychtoolbox (Brainard and Vision). The experiment presentation script, all analysis code, and stimuli are made available (see code and stimuli folder). The data is made available both in raw form (see each participant’s file) and after processing (see derivatives).
Cohort#
Dataset Statistics#
Age distribution by gender (n=40, range 17–40 yr, mean 21.8 yr)
Sex composition
Channel counts: 64 ch (n=119 recordings)
Sampling frequencies: 2048.0 Hz (n=119 recordings)
Total recording duration: 64 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-02 · task-touchdecoding
Showing one representative recording out of
40 subjects and 119 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.
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 |
Vicarious touch: overlapping neural patterns between seeing and feeling touch |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Sophie Smit, Denise Moerel, Regine Zopf, Anina N Rich |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004563,
title = {Vicarious touch: overlapping neural patterns between seeing and feeling touch},
author = {Sophie Smit and Denise Moerel and Regine Zopf and Anina N Rich},
doi = {10.18112/openneuro.ds004563.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds004563.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004563 · Smit2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Vicarious touch: overlapping neural patterns between seeing and feeling touch
- Study:
ds004563(OpenNeuro)- Author (year):
Smit2023- Canonical:
—
Also importable as:
DS004563,Smit2023.Modality:
eeg; Experiment type:Perception; Subject type:Other. Subjects: 40; recordings: 119; 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/ds004563 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004563 DOI: https://doi.org/10.18112/openneuro.ds004563.v1.0.1 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004563 >>> dataset = DS004563(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/ds004563").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 ds004563 to reproduce the tutorial on this dataset.
Citation
Sophie Smit, Denise Moerel, Regine Zopf, Anina N Rich (n.d.). Vicarious touch: overlapping neural patterns between seeing and feeling touch. 10.18112/openneuro.ds004563.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004563.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset