DS005079#

The Effects of Directed Therapeutic Intent on Live and Damaged Cells

Access recordings and metadata through EEGDash.

Citation: Lorenzo Cohen, Arnaud Delorme, Peiying Yang, Andrew Cusimano, Sharmistha Chakraborty, Phuong Nguyen, Defeng Deng, Shafaqmuhammad Iqbal, Monica Nelson, Chris Fields (2024). The Effects of Directed Therapeutic Intent on Live and Damaged Cells. 10.18112/openneuro.ds005079.v2.0.0

Modality: eeg Subjects: 1 Recordings: 210 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

Dataset Information#

Dataset ID	`DS005079`
Title	The Effects of Directed Therapeutic Intent on Live and Damaged Cells
Year	2024
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	doi:10.18112/openneuro.ds005079.v2.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

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: 1
Recordings: 210
Tasks: 1

Channels & sampling rate

Channels: 65 (60), 64 (60)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005079 class to access this dataset programmatically.

class eegdash.dataset.DS005079(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005079. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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/ds005079 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005079

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, 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