DS006110: eeg dataset, 65 subjects#
PsiConnect
Citation: Adeel Razi (2025). PsiConnect. 10.18112/openneuro.ds006110.v1.2.0
65-participant EEG dataset — PsiConnect.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006110
dataset = DS006110(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006110(cache_dir="./data", subject="01")
Advanced query
dataset = DS006110(
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{ds006110,
title = {PsiConnect},
author = {Adeel Razi},
doi = {10.18112/openneuro.ds006110.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds006110.v1.2.0},
}
About This Dataset#
PsiConnect is a large-scale neuroimaging study designed to investigate context-dependent neural and subjective effects of psilocybin using multimodal neuroimaging. It combines functional, structural, and diffusion-weighted MRI with EEG to examine brain activity in 62 participants before and after a 19 mg dose of psilocybin. The design includes resting-state scans and three naturalistic conditions: guided meditation, music listening, and movie watching. Half of the cohort underwent an 8-week meditation training program, enabling exploration of interactions among meditation, psilocybin, and brain function. fMRI data was obtained through multi-echo fMRI, enhancing signal-to-noise ratio and reducing susceptibility artifacts to improve reliability. A comprehensive battery of behavioural and self-report measures captured acute and longitudinal cognitive and subjective effects, with follow-ups to one year post-administration. The large sample, multimodal imaging, contextual diversity, and behavioural follow-ups enable study of psilocybin-induced brain and behaviour changes with unprecedented comprehensiveness and reliability. Data is curated according to open science principles to ensure accessibility and compatibility with established neuroimaging pipelines, making PsiConnect a valuable, reusable resource for cognitive and computational neuroscience.
Please cite both:
Novelli, L. et al. (2025). PsiConnect: A Multimodal Neuroimaging Study of Psilocybin-Induced Changes in Brain and Behaviour. https://doi.org/10.1101/2025.04.11.643415
Stoliker, D. et al. (2025). Psychedelics Align Brain Activity with Context. 2025. https://doi.org/10.1101/2025.03.09.642197
Cohort#
Dataset Statistics#
Age distribution by gender (n=65, range 19–54 yr, mean 37.8 yr)
Channel counts (ch)
Sampling frequencies: 500.0 Hz (n=124 recordings)
Total recording duration: 46 h
Signal · Electrodes & live trace#
Live trace viewer — sub-PC211 · ses-02 · task-series
Showing one representative recording out of
65 subjects and 124 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 · 64 sensors — 64 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.
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 |
PsiConnect |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2025 |
Authors |
Adeel Razi |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006110,
title = {PsiConnect},
author = {Adeel Razi},
doi = {10.18112/openneuro.ds006110.v1.2.0},
url = {https://doi.org/10.18112/openneuro.ds006110.v1.2.0},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS006110(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
PsiConnect
- Study:
ds006110(OpenNeuro)- Author (year):
nan- Canonical:
—
Also importable as:
DS006110,nan.Modality:
eeg. Subjects: 65; recordings: 124; 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/ds006110 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006110 DOI: https://doi.org/10.18112/openneuro.ds006110.v1.2.0
Examples
>>> from eegdash.dataset import DS006110 >>> dataset = DS006110(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 ds006110 to reproduce the tutorial on this dataset.
Citation
Adeel Razi (2025). PsiConnect. 10.18112/openneuro.ds006110.v1.2.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006110.v1.2.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset