Evidence map›Paper›PMID 41381487›Full record

ArticleScientific data2025

Tactile-evoked EEG Dataset for Natural Perception Using an Integrated Stimulation-Recording Framework.

Linna Mao, Peishuai Liu, Jingyang Li, Xuhui Wang, Hengjie Su, Xin Zhang, Jing Sun, Ting Li

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Linna Mao *Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China.
Peishuai Liu *Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China.
Jingyang LiInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China.
Xuhui WangInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China.
Hengjie SuInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China.
Xin ZhangInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China.
Jing SunInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China.
Ting LiInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300192, China. liting@bme.pumc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing demand for assistive living and medical technologies in aging societies has driven advancements in tactile-evoked Brian Computer Interface (BCI) systems, offering an alternative to traditional visual and auditory-based BCI systems. However, the development of such systems is constrained by challenges in quantifying tactile sensations and a lack of diverse datasets. This study presents an integrated system enabling natural tactile perception during dynamic touch experience while simultaneously recording electroencephalographic (EEG) responses. EEG signals were collected from 10 healthy participants (64 channels, 1000 Hz) in natural tactile perception tasks involving contact with three distinct materials. Preliminary analysis revealed significant differences in the P300 peak latency and amplitude between tactile conditions, highlighting the unique characteristics of tactile-evoked EEG signals. A three-class classification using Common Spatial Pattern (CSP) and Support Vector Machine (SVM) models demonstrated above-chance accuracy. This tactile-evoked EEG dataset provides a valuable resource for seeking tactile-related neural mechanisms and driving the practical application of BCI systems, offering a pathway to improved user experiences and functionality in real-world scenarios.

Indexed as

Brain-Computer InterfacesElectroencephalographyTouchTouch PerceptionAdultFemaleHumansMaleSupport Vector Machine

Identifiers

PMID41381487
PMCPMC12739163

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.