DS005048: eeg dataset, 35 subjects#
40Hz Auditory Entrainment
Citation: Mojtaba Lahijanian, Hamid Aghajan, Zahra Vahabi (—). 40Hz Auditory Entrainment. 10.18112/openneuro.ds005048.v1.0.1
35-participant EEG dataset — 40Hz Auditory Entrainment.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005048
dataset = DS005048(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005048(cache_dir="./data", subject="01")
Advanced query
dataset = DS005048(
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{ds005048,
title = {40Hz Auditory Entrainment},
author = {Mojtaba Lahijanian and Hamid Aghajan and Zahra Vahabi},
doi = {10.18112/openneuro.ds005048.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds005048.v1.0.1},
}
About This Dataset#
Introduction
This experiment was designed to entrain the brain oscillations through synthetic auditory stimulation conducted on a group of elderly suffering from dementia. Recently, gamma entrainment has been proposed and shown effective in improving several symptoms of Alzheimer’s Diseases (AD). The aim of this study is to investigate the effect of entrainment on brain oscillations using EEG signal recording during the auditory brain stimulation. This study was approved by the Review Board of Tehran University of Medical Sciences (Approval ID: IR.TUMS.MEDICINE.REC.1398.524). All methods were performed in accordance with the relevant guidelines and regulations, and all participants provided informed consent before participating and were free to withdraw at any time. To accommodate participants who preferred a shorter duration of data gathering, we designed both short and long sessions for entrainment. This approach aimed to minimize inconvenience for the participants who were less inclined to engage in lengthy procedures.
Entrainment session and auditory stimulation
Each session involved the presentation of a multi-trial auditory stimulus while simultaneously recording EEG data from the participant. To deliver the auditory stimulus, two speakers were placed in front of the participant 50cm apart from each other and directly pointed at the participant’s ears at a distance of 50cm. The sound intensity was around -40dB within a fixed range for all participants. To ascertain adequate hearing ability of the participants and to ensure individual comfort, each participant was asked before commencing the task if the sound was at a comfortable level, and adjustments were made to the volume. The auditory stimulus was a 5kHz carrier tone amplitude modulated with a 40Hz rectangular wave (40Hz On and Off cycles). Since a 40Hz tone cannot be easily heard, the 5KHz carrier frequency was used to render the 40Hz pulse train audible. In order to minimize the effect of the carrier sound, the duty cycle of the modulating 40Hz waveform was set to 4% (1ms of the 25ms cycle was On). The auditory stimulant was generated in MATLAB and played as a .wav file. This file consisted of multiple trials, with each trial lasting 40sec and interleaved by 20sec of rest (silence). The short session included six trials, while the long session comprised ten trials of the stimulus. EEG recording and preprocessing All EEG data were recorded using 19 monopolar channels based on the standard 10/20 system. For the short session, the reference electrodes were placed on the earlobes, while for the long session, referencing was done to the FCz channel. Notably, referencing to the average was implemented during preprocessing, ensuring data integrity and minimizing potential interference. The sampling rate was set to 250Hz, and the impedance of the electrodes was kept under 20kΩ. During the experiment, participants were seated comfortably with open eyes in a quiet room, and they were instructed to relax their body to avoid muscle artifacts and to move their head as little as possible.
Data from all the participants were preprocessed identically following Makoto’s preprocessing pipeline: Highpass filtering above 1Hz; removal of the line noise; rejecting potential bad channels; interpolating rejected channels; re-referencing data to the average; artifact subspace reconstruction (ASR); re-referencing data to the average again; estimating the brain source activity using independent component analysis (ICA); dipole fitting; rejecting bad dipoles (sources) for further cleaning the data. These preprocessing steps were performed using EEGLab toolbox in MATLAB.
Cohort#
Dataset Statistics#
Age distribution (n=35, range 54–89 yr, mean 72.7 yr · sex per subject not reported)
Sex composition
Channel counts: 19 ch (n=35 recordings)
Sampling frequencies: 250.0 Hz (n=35 recordings)
Total recording duration: 5 h 12 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-40HzAuditoryEntrainment
Showing one representative recording out of
35 subjects and 35 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 |
40Hz Auditory Entrainment |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Mojtaba Lahijanian, Hamid Aghajan, Zahra Vahabi |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005048,
title = {40Hz Auditory Entrainment},
author = {Mojtaba Lahijanian and Hamid Aghajan and Zahra Vahabi},
doi = {10.18112/openneuro.ds005048.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds005048.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005048 · Lahijanian2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005048(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
40Hz Auditory Entrainment
- Study:
ds005048(OpenNeuro)- Author (year):
Lahijanian2024- Canonical:
—
Also importable as:
DS005048,Lahijanian2024.Modality:
eeg. Subjects: 35; recordings: 35; 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/ds005048 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005048 DOI: https://doi.org/10.18112/openneuro.ds005048.v1.0.1 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS005048 >>> dataset = DS005048(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/ds005048").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 ds005048 to reproduce the tutorial on this dataset.
Citation
Mojtaba Lahijanian, Hamid Aghajan, Zahra Vahabi (n.d.). 40Hz Auditory Entrainment. 10.18112/openneuro.ds005048.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.ds005048.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset