DS004563#

Vicarious touch: overlapping neural patterns between seeing and feeling touch

Access recordings and metadata through EEGDash.

Citation: Sophie Smit, Denise Moerel, Regine Zopf, Anina N Rich (2023). Vicarious touch: overlapping neural patterns between seeing and feeling touch. 10.18112/openneuro.ds004563.v1.0.1

Modality: eeg Subjects: 40 Recordings: 377 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

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).

Dataset Information#

Dataset ID

DS004563

Title

Vicarious touch: overlapping neural patterns between seeing and feeling touch

Year

2023

Authors

Sophie Smit, Denise Moerel, Regine Zopf, Anina N Rich

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004563.v1.0.1

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},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

  • Subjects: 40

  • Recordings: 377

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 2048.0

  • Duration (hours): 0.0

Tags

  • Pathology: Other

  • Modality: Multisensory

  • Type: Perception

Files & format

  • Size on disk: 100.9 GB

  • File count: 377

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004563.v1.0.1

Provenance

API Reference#

Use the DS004563 class to access this dataset programmatically.

class eegdash.dataset.DS004563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004563. 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

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/ds004563 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004563

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#