Evidence map›Paper›PMID 41991955›Full record

ArticleScientific data2026

An Open Non-Invasive EEG Dataset for Spontaneous Auditory Attention Switch Decoding.

Xuefei Wang, Yuting Ding, Yueting Ban, Lei Wang, Fei Chen

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. 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

5 authors.

Xuefei WangDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, P. R. China.
Yuting DingDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, P. R. China.
Yueting BanDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, P. R. China.
Lei WangSchool of Electronics and Communication Engineering, Guangzhou University, Guangzhou, Guangdong, 510000, P. R. China.
Fei ChenDepartment of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, P. R. China. fchen@sustech.edu.cn.

Funding

the National Key Research and Development Program of China No. 2023YFF1203502
6 · The paper itself

Abstract

Auditory brain-computer interfaces (BCIs) based on non-invasive electrodes offer significant potential for advancing the understanding of selective auditory attention mechanisms and enabling natural human-computer interaction. Despite growing interest in auditory attention research, publicly available datasets focused on spontaneous auditory attention switching remain limited, particularly those with high-quality electroencephalography (EEG) recordings in realistic listening environments. To address this gap, we present the Auditory Attention Switching Dataset (AASD), a non-invasive EEG dataset designed to investigate spontaneous selective auditory attention switching during naturalistic auditory processing. The dataset captures both sustained attention and spontaneous attention switching events through EEG signals. A baseline decoding model is introduced to verify data integrity and demonstrate its potential for practical applications. This open-access resource lays the foundation for developing algorithms for spontaneous auditory attention switching and advancing research in natural-scenario auditory BCIs.

Indexed as

AttentionAuditory PerceptionBrain-Computer InterfacesElectroencephalographyAlgorithmsHumans

Identifiers

PMID41991955
PMCPMC13261058

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.