DS005815#

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 (2025). A Human EEG Dataset for Multisensory Perception and Mental Imagery. 10.18112/openneuro.ds005815.v2.0.1

Modality: eeg Subjects: 20 Recordings: 616 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005815

dataset = DS005815(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 = DS005815(cache_dir="./data", subject="01")

Advanced query

dataset = DS005815(
    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{ds005815,
  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 = {10.18112/openneuro.ds005815.v2.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005815.v2.0.1},
}

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.

Dataset Information#

Dataset ID	`DS005815`
Title	A Human EEG Dataset for Multisensory Perception and Mental Imagery
Year	2025
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	doi:10.18112/openneuro.ds005815.v2.0.1
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: 20
Recordings: 616
Tasks: 5

Channels & sampling rate

Channels: 30
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005815 class to access this dataset programmatically.

class eegdash.dataset.DS005815(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005815. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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/ds005815 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005815

Examples

>>> from eegdash.dataset import DS005815
>>> dataset = DS005815(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