Evidence map›Paper›PMID 41028775›Full record

ArticleScientific data2025

A Human EEG Dataset for Multisensory Perception and Mental Imagery.

Yan-Han Chang, Hsi-An Chen, Min-Jiun Tsai, Chun-Lung Tseng, Ching-Huei Lo, Kuan-Chih Huang, Chun-Shu Wei

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yan-Han ChangDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.ORCID 0009-0007-5371-273X
Hsi-An ChenDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Min-Jiun TsaiInstitute of Mathematical Modeling and Scientific Computing, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Chun-Lung TsengInstitute of Education, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Ching-Huei LoInstitute of Education, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Kuan-Chih HuangBrain Science and Technology Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Chun-Shu WeiDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. wei@nycu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The YOTO (You Only Think Once) dataset presents a human electroencephalog- raphy (EEG) resource for exploring multisensory perception and mental imagery. The study enrolled 26 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 subjec- tive perceptual strength. Technical validation involved event-related potentials (ERPs) and power spectral density (PSD) analyses, which demonstrated the reli- ability of the data and confirmed distinct neural responses across stimuli. This dataset aims to foster studies on neural decoding, perception, and cognitive mod- eling, and it is publicly accessible for researchers who seek to advance multimodal mental imagery research and related applications.

Indexed as

ElectroencephalographyImaginationAdultAuditory PerceptionEvoked PotentialsFemaleHumansMaleVisual Perception

Identifiers

PMID41028775
PMCPMC12484789

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.