ON005815: eeg dataset, 20 subjects#
A Human EEG Dataset for Multisensory Perception and Mental Imagery
Access recordings and metadata through EEGDash.
Citation: Yan-Han Chang, Hsi-An Chen, Min-Jiun Tsai, Chun-Lung Tseng, Ching-Huei Lo, Kuan-Chih Huang, Chun-Shu Wei (—). A Human EEG Dataset for Multisensory Perception and Mental Imagery. 10.82901/nemar.on005815
Modality: eeg Subjects: 20 Recordings: 103 License: CC0 Source: nemar
Metadata: Complete (100%)
20-participant EEG dataset — A Human EEG Dataset for Multisensory Perception and Mental Imagery.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON005815
dataset = ON005815(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON005815(cache_dir="./data", subject="01")
Advanced query
dataset = ON005815(
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{on005815,
title = {A Human EEG Dataset for Multisensory Perception and Mental Imagery},
author = {Yan-Han Chang and Hsi-An Chen and Min-Jiun Tsai and Chun-Lung Tseng and Ching-Huei Lo and Kuan-Chih Huang and Chun-Shu Wei},
doi = {10.82901/nemar.on005815},
url = {https://doi.org/10.82901/nemar.on005815},
}
About This Dataset#
The YOTO (You Only Think Once) dataset presents a human electroencephalography (EEG) resource for exploring multisensory perception and mental imagery. The study enrolled 20 participants who performed tasks involving both unimodal and multimodal stimuli. Researchers collected high-resolution EEG signals at a 1000 Hz sampling rate to capture high-temporal-resolution neural activity related to internal mental representations. The protocol incorporated visual, auditory, and combined cues to investigate the integration of multiple sensory modalities, and participants provided self-reported vividness ratings that indicate subjective perceptual strength. Technical validation involved event-related potentials (ERPs) and power spectral density (PSD) analyses, which demonstrated the reliability of the data and confirmed distinct neural responses across stimuli. This dataset aims to foster studies on neural decoding, perception, and cognitive modeling, and it is publicly accessible for researchers who seek to advance multimodal mental imagery research and related applications.
Cohort#
Dataset Statistics#
Channel counts: 31 ch (n=103 recordings)
Sampling frequencies: 1000.0 Hz (n=103 recordings)
Total recording duration: 3 h 59 min
Signal · Electrodes & live trace#
Live trace viewer — sub-14 · ses-1 · task-rest1
Showing one representative recording out of
20 subjects and 103 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 |
A Human EEG Dataset for Multisensory Perception and Mental Imagery |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Yan-Han Chang, Hsi-An Chen, Min-Jiun Tsai, Chun-Lung Tseng, Ching-Huei Lo, Kuan-Chih Huang, Chun-Shu Wei |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on005815,
title = {A Human EEG Dataset for Multisensory Perception and Mental Imagery},
author = {Yan-Han Chang and Hsi-An Chen and Min-Jiun Tsai and Chun-Lung Tseng and Ching-Huei Lo and Kuan-Chih Huang and Chun-Shu Wei},
doi = {10.82901/nemar.on005815},
url = {https://doi.org/10.82901/nemar.on005815},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON005815(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
A Human EEG Dataset for Multisensory Perception and Mental Imagery
- Study:
on005815(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON005815.Modality:
eeg; Subject type:Unknown. Subjects: 20; recordings: 103; 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
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/on005815 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on005815 DOI: https://doi.org/10.82901/nemar.on005815
Examples
>>> from eegdash.dataset import ON005815 >>> dataset = ON005815(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.pytorch
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 on005815 to reproduce the tutorial on this dataset.
Citation
Yan-Han Chang, Hsi-An Chen, Min-Jiun Tsai, Chun-Lung Tseng, Ching-Huei Lo, … (n.d.). A Human EEG Dataset for Multisensory Perception and Mental Imagery. 10.82901/nemar.on005815
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on005815.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset