Evidence map›Paper›PMID 41717878›Full record

ArticleThe European journal of neuroscience2026

Active Task Engagement Enhances Auditory Brain-Behaviour Prediction From Single-Trial EEG Compared With Passive Listening.

Zhaonan Ma, Xiaoyu Wang, Xiao Yang, Chao Guo, Tommi Kärkkäinen, Fengyu Cong

Abstract read
In one paragraph

Article in The European journal of neuroscience, 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Zhaonan MaSchool of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China.ORCID 0009-0007-8453-5916
Xiaoyu WangWestern Institute of Neuroscience, Western University, London, Canada.ORCID 0000-0002-0828-6328
Xiao YangSchool of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China.
Chao GuoSchool of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China.ORCID 0009-0003-2946-620X
Tommi KärkkäinenFaculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.
Fengyu CongSchool of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China.

Funding

China Scholarship Council 202106060036
6 · The paper itself

Abstract

Auditory neural processing during active task engagement and passive listening reflects distinct task contexts with potentially different behavioural relevance. While both contexts elicit deviance-related responses, it remains unclear, which yields neural measures that more reliably predict individual differences in behavioural performance. To address this question, we employed a multi-feature auditory paradigm in which frequency, duration, and intensity deviants were presented under passive (no response required) and active (explicit detection required) conditions. EEG was recorded from 47 participants; passive listening was characterized by a prominent mismatch negativity (MMN), whereas active discrimination was characterized by an additional P3b component. Beyond conventional ERP measures, we quantified individual-level neural discriminability using EEGNet, a neural-network-based classifier, by classifying deviant versus standard single-trial epochs and deriving cross-validated decoding accuracy. Behavioural performance was quantified using an efficiency score (ES) that integrates hit rate and reaction time. Participants were stratified into high- and low-performance groups based on a median split of ES. Results showed that the expected MMN during passive listening and the P3b during active discrimination were elicited, as confirmed by spatiotemporal cluster-based permutation analysis. Furthermore, decoding accuracy derived from the active discrimination condition robustly separated high- and low-performance groups (Group × Task: F = 29.62, p < 0.001) and predicted behavioural efficiency across individuals (r = 0.53, p < 0.01). In contrast, passive-listening decoding showed reduced overall discriminability and minimal group separation. Together, these findings indicate that task engagement amplifies the behavioural relevance of single-trial neural discriminability, enabling stronger auditory brain-behaviour prediction than passive listening.

Indexed as

Auditory PerceptionBrainAcoustic StimulationAdultElectroencephalographyFemaleHumansMaleProblem-Based LearningTask Performance and AnalysisYoung Adultbrain–behaviour couplingevent‐related potentials (ERPs)mismatch negativity (MMN)single‐trial EEG decoding

Identifiers

PMID41717878
PMCPMC12921835

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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.