DS003805: eeg dataset, 1 subjects#
Multisensory Gamma Entrainment
Citation: Mojtaba Lahijanian, Mohammad Javad Sedghizadeh, Hamid Aghajan (—). Multisensory Gamma Entrainment. 10.18112/openneuro.ds003805.v1.0.0
1-participant EEG dataset — Multisensory Gamma Entrainment.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003805
dataset = DS003805(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003805(cache_dir="./data", subject="01")
Advanced query
dataset = DS003805(
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{ds003805,
title = {Multisensory Gamma Entrainment},
author = {Mojtaba Lahijanian and Mohammad Javad Sedghizadeh and Hamid Aghajan},
doi = {10.18112/openneuro.ds003805.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003805.v1.0.0},
}
About This Dataset#
Introduction
This experiment was designed to study the effects of different sensory modalities (auditory, visual, and audio-visual) on brain entrainment. The EEG data was collected from a young healthy volunteer (23 years old male). Recently, gamma entrainment based on individual (auditory or visual) sensory stimulation as well as simultaneous auditory and visual stimulation have been proposed and shown effective in improving several symptoms of Alzheimer’s Diseases (AD) in mice and humans. The aim of this study is to investigate the effect of different modalities in producing synchronized brain oscillations. The task is composed of three epochs of auditory, visual, and audio-visual stimulations respectively, each lasting for 40sec in one session.
Auditory stimulation
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 set to around -40dB. Before starting the task, the participant was asked if the volume was loud enough and the sound volume was set at a comfortable level for him. The auditory stimulus was a 5kHz carrier tone amplitude modulated with a 40Hz rectangular wave (40Hz On and Off cycles). Since a 40Hz audio signal 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 40sec of stimulus.
Visual stimulation
The visual stimulant was a 20Hz flickering white light produced by an array of LEDs and reflected from a white wall at 50cm distance in front of the participant (open eyes) with 50% On cycles (duty cycle = 50%) flickering for 40sec. Due to the presence of harmonic frequencies in the pulse train of the stimulus, the 20Hz stimulant is able to drive 40Hz oscillations in the brain.
EEG recording and preprocessing
The EEG data were recorded using 19 monopolar channels in the standard 10/20 system referenced to the earlobes, sampled at 500Hz, and the impedance of the electrodes was kept under 20kOhm.
Data from all three epochs 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 MATLAB toolbox.
Instructions
During the experiment, participant was seated comfortably with open eyes in a quiet room. He was instructed to relax his body to avoid muscle artifacts and move his head as little as possible. The participant was free to take a rest after each epoch but the EEG cap was not taken off.
Cohort#
Dataset Statistics#
Age distribution (n=1, range 23–23 yr, mean 23.0 yr · sex per subject not reported)
Sex composition
Channel counts: 19 ch (n=1 recordings)
Sampling frequencies: 500.0 Hz (n=1 recordings)
Total recording duration: 2 min
Signal · Electrodes & live trace#
Live trace viewer — sub-1 · task-MultisensoryGammaEntrainment
Showing one representative recording out of
1 subjects and 1 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 · 19 sensors — 19 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 |
Multisensory Gamma Entrainment |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Mojtaba Lahijanian, Mohammad Javad Sedghizadeh, Hamid Aghajan |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003805,
title = {Multisensory Gamma Entrainment},
author = {Mojtaba Lahijanian and Mohammad Javad Sedghizadeh and Hamid Aghajan},
doi = {10.18112/openneuro.ds003805.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003805.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003805 · Lahijanian2021_Multisensoryeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003805(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Multisensory Gamma Entrainment
- Study:
ds003805(OpenNeuro)- Author (year):
Lahijanian2021_Multisensory- Canonical:
—
Also importable as:
DS003805,Lahijanian2021_Multisensory.Modality:
eeg. Subjects: 1; recordings: 1; 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/ds003805 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003805 DOI: https://doi.org/10.18112/openneuro.ds003805.v1.0.0 NEMAR citation count: 3
Examples
>>> from eegdash.dataset import DS003805 >>> dataset = DS003805(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/ds003805").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 ds003805 to reproduce the tutorial on this dataset.
Citation
Mojtaba Lahijanian, Mohammad Javad Sedghizadeh, Hamid Aghajan (n.d.). Multisensory Gamma Entrainment. 10.18112/openneuro.ds003805.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003805.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset