EEGdash›OpenNeuro›DS004840

Iss. 4840 · 9 subjects · 51 recordings · CC0

Dataset Brief · Dataset of electrophysiological signals (EEG, ECG, EMG) durin…

DS004840: eeg dataset, 9 subjects#

Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.

Citation: Jose Cordoba-Silva, Rafael Maya, Mario Valderrama, Luis Felipe Giraldo, William Betancourt-Zapata, Andrés Salgado-Vascob, Juliana Marín-Sánchez, Viviana Gómez-Ortega, Mark Ettenberger (20). Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.. 10.18112/openneuro.ds004840.v1.0.1

9-participant EEG dataset — Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit..

Data & curation Jose Cordoba-Silva · Rafael Maya · Mario Valderrama · Luis Felipe Giraldo · William Betancourt-Zapata · Andrés Salgado-Vascob · …
Year 20 · Distributed via OpenNeuro

EEG · 10 (45), 9 (3), 8 (3) ch256, 512, 1024 HzBIDS 1.8.03 tasks2 sessionsOtherAuditoryClinical/Intervention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004840

dataset = DS004840(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 = DS004840(cache_dir="./data", subject="01")

Advanced query

dataset = DS004840(
    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{ds004840,
  title = {Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.},
  author = {Jose Cordoba-Silva and Rafael Maya and Mario Valderrama and Luis Felipe Giraldo and William Betancourt-Zapata and Andrés Salgado-Vascob and Juliana Marín-Sánchez and Viviana Gómez-Ortega and Mark Ettenberger},
  doi = {10.18112/openneuro.ds004840.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004840.v1.0.1},
}

§ 02Study · The README

About This Dataset#

This dataset forms part of an ongoing single-site Randomized Clinical Trial (RCT) involving adult burn patients admitted to the Intensive Care Unit (ICU). The key details of the study include:

Participants: The study encompasses 82 adult burn patients admitted to the ICU.
Randomization: Participants were randomly assigned to either an intervention group or a control group in a 1:1 ratio. The intervention group received standard care in addition to a maximum of six Music therapy sessions provided by a certified music therapist over a 2-week period.
Electrophysiological Measures: Electrophysiological measures were taken from a subset of 9 participants in the intervention group (11%).
Ethics and Registration: The study was approved by the ethics committee of the Fundación Santa Fe de Bogotá (FSFB) with approval IDs CCEI-11234-2019 and CCEI-11971-2020. It is registered on Clinicaltrials.gov under the identifier NCT04571255.
Informed Consent: All participants provided informed consent to participate in the study.

For more detailed information about the study, please refer to the associated article titled “Article not submitted”, DOI: ‘-Insert Link-’

Last Update: 23/10/2023 Author: Jose Gabriel Cordoba Silva

Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit - README

Table of Contents

1. Experimental Design - 1.1 Study Overview - 1.2 Electrophysiological Measurements

2. Pain Perception and Anxiety-Depression Levels

3. Code GIT repository

1. Experimental Design

1.1 Study Overview

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=9, range 18–65 yr, mean 33.6 yr)

152025355565

Female · 1Male · 8

Sex composition

Female
1
Male
8
F : M ratio
0.12 : 1

11% female · n = 9 subjects with reported sex.

Channel counts (ch)

8910

Sampling frequencies (Hz)

2565121024

Total recording duration: 10 h 56 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 10 (45), 9 (3), 8 (3) ch · EEG · 256, 512, 1024 Hz · 9 subjects, 51 recordings

Live trace viewer — sub-08 · ses-02 · task-preMusicTherapy

Showing one representative recording out of 9 subjects and 51 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS004840(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)CordobaSilva2023

Canonical—

Importable asDS004840 · CordobaSilva2023

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS004840(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.

Study:: ds004840 (OpenNeuro)
Author (year):: CordobaSilva2023
Canonical:: —

Also importable as: DS004840, CordobaSilva2023.

Modality: eeg. Subjects: 9; recordings: 51; tasks: 3.

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/ds004840 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004840 DOI: https://doi.org/10.18112/openneuro.ds004840.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004840
>>> dataset = DS004840(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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FacePre-bundled mirror at EEGDash/ds004840 · pull with datasets.load_dataset("EEGDash/ds004840").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004840.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 ds004840 to reproduce the tutorial on this dataset.

Citation

Jose Cordoba-Silva, Rafael Maya, Mario Valderrama, Luis Felipe Giraldo, William Betancourt-Zapata, … (20). Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.. 10.18112/openneuro.ds004840.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.ds004840.v1.0.1.

Related & sibling datasets

DS003626EEG · 10 subj DS002723EEG · 8 subj DS002724EEG · 10 subj DS004018EEG · 16 subj DS001785EEG · 18 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004840.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004840`
Title	Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.
Author (year)	`CordobaSilva2023`
Canonical	—
Importable as	`DS004840`, `CordobaSilva2023`
Year	20
Authors	Jose Cordoba-Silva, Rafael Maya, Mario Valderrama, Luis Felipe Giraldo, William Betancourt-Zapata, Andrés Salgado-Vascob, Juliana Marín-Sánchez, Viviana Gómez-Ortega, Mark Ettenberger
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004840.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS004840: eeg dataset, 9 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#