DS005079: eeg dataset, 1 subjects#
The Effects of Directed Therapeutic Intent on Live and Damaged Cells
Citation: Lorenzo Cohen, Arnaud Delorme, Peiying Yang, Andrew Cusimano, Sharmistha Chakraborty, Phuong Nguyen, Defeng Deng, Shafaqmuhammad Iqbal, Monica Nelson, Chris Fields (—). The Effects of Directed Therapeutic Intent on Live and Damaged Cells. 10.18112/openneuro.ds005079.v2.0.0
1-participant EEG dataset — The Effects of Directed Therapeutic Intent on Live and Damaged Cells.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005079
dataset = DS005079(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005079(cache_dir="./data", subject="01")
Advanced query
dataset = DS005079(
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{ds005079,
title = {The Effects of Directed Therapeutic Intent on Live and Damaged Cells},
author = {Lorenzo Cohen and Arnaud Delorme and Peiying Yang and Andrew Cusimano and Sharmistha Chakraborty and Phuong Nguyen and Defeng Deng and Shafaqmuhammad Iqbal and Monica Nelson and Chris Fields},
doi = {10.18112/openneuro.ds005079.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds005079.v2.0.0},
}
About This Dataset#
Summary: In this case study, a self-described practitioner of energy medicine (PEM) participated in a study, engaging in multiple (n=60) treatment and control (non-treatment) sessions under double-blind conditions.
Protocol: Data were collected during 40 sessions over 10 days, with ten sessions of about 25 minutes daily. Each session was comprised of one file divided into five segments. First, there was a 2-minute control period where the PEM rested in the absence of cells (BaselinePre) for 2 minutes. Next, the cells (alive or control) were brought in, and the PEM conducted a 5-minute treatment of the cells while remaining still (TreatmentFirst5min). Next, the PEM performed another 5-minute treatment of the cells, but movement was allowed (Treatment 2). During a third treatment period (TreatmentMid5min), the PEM remained still while treating the cells, as in first treatment period (TreatmentLast5min). Finally, the cells were removed from the PEM’s vicinity, and physiology data were collected for another 2-minute control period (BaselinePost). The PEM was fully blind to the type of cells presented to him, and cell type presentation to the PEM was randomized. The experimenter presenting the cell to the PEM was also blind to the type of cells. In 40 sessions, live cells were presented to the PEM (CellPresent condition). In 10 sessions, no cells (medium only) were presented to the PEM. In the other ten sessions, dead cells (x-rayed) were presented to the PEM (Control1 and Control2 conditions). In order to have control samples for the cellular outcomes and control for the passage of time and potential effects of the equipment, 40 matching set of cells were treated in a different location by a sham therapist (these are available in the behavioral files (BEH) as control cell measures. Data curators: Data acquired at the MD Anderson Cancer Research Center
Cohort#
Dataset Statistics#
Channel counts: 65 ch (n=60 recordings)
Sampling frequencies: 500.0 Hz (n=60 recordings)
Total recording duration: 3 h 48 min
Signal · Electrodes & live trace#
Live trace viewer — sub-001 · ses-10 · task-BaselinePostControl2
Showing one representative recording out of
1 subjects and 60 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 |
The Effects of Directed Therapeutic Intent on Live and Damaged Cells |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Lorenzo Cohen, Arnaud Delorme, Peiying Yang, Andrew Cusimano, Sharmistha Chakraborty, Phuong Nguyen, Defeng Deng, Shafaqmuhammad Iqbal, Monica Nelson, Chris Fields |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005079,
title = {The Effects of Directed Therapeutic Intent on Live and Damaged Cells},
author = {Lorenzo Cohen and Arnaud Delorme and Peiying Yang and Andrew Cusimano and Sharmistha Chakraborty and Phuong Nguyen and Defeng Deng and Shafaqmuhammad Iqbal and Monica Nelson and Chris Fields},
doi = {10.18112/openneuro.ds005079.v2.0.0},
url = {https://doi.org/10.18112/openneuro.ds005079.v2.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005079 · Cohen2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005079(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
The Effects of Directed Therapeutic Intent on Live and Damaged Cells
- Study:
ds005079(OpenNeuro)- Author (year):
Cohen2024- Canonical:
—
Also importable as:
DS005079,Cohen2024.Modality:
eeg. Subjects: 1; recordings: 60; tasks: 15.- 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/ds005079 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005079 DOI: https://doi.org/10.18112/openneuro.ds005079.v2.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS005079 >>> dataset = DS005079(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/ds005079").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 ds005079 to reproduce the tutorial on this dataset.
Citation
Lorenzo Cohen, Arnaud Delorme, Peiying Yang, Andrew Cusimano, Sharmistha Chakraborty, … (n.d.). The Effects of Directed Therapeutic Intent on Live and Damaged Cells. 10.18112/openneuro.ds005079.v2.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005079.v2.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset