Literature DB >> 33901488

Neural mechanism for dynamic distractor processing during video target detection: Insights from time-varying networks in the cerebral cortex.

Xiyu Song1, Ying Zeng2, Li Tong3, Jun Shu4, Huimin Li5, Bin Yan6.   

Abstract

In dynamic video target detection tasks, distractors may suddenly appear due to the dynamicity of the visual scene and the uncertainty of the visual information, strongly influencing participants' attention and target detection performance. Moreover, the neural mechanism that accounts for dynamic distractor processing remains unknown, which makes it difficult to compensate for in EEG-based video target detection. Here, cortical activities with high spatiotemporal resolution were reconstructed using the source localization method. The time-varying networks among important brain regions in different cognitive phases, including information integration, decision-making, and execution, were identified to investigate the neural mechanism of dynamic distractor processing. The experimental results indicated that dynamic distractors could induce a P3-like component. In addition, there was obvious asymmetry between the two hemispheres during video target detection. Specifically, the brain responses induced by dynamic distractors were weak and more concentrated in the left hemisphere during the information integration phase; left superior frontal gyrus activity related to preparation for the presence of distractors was critical, while the attention network and primary visual network, especially in the left visual pathway, were more active for dynamic targets during the decision-making phase. These findings provide guidance for designing an effective EEG-based model for dynamic video target detection.
Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Distractor; Electroencephalogram; Neural mechanism; Source estimation; Time-varying network

Mesh:

Year:  2021        PMID: 33901488     DOI: 10.1016/j.brainres.2021.147502

Source DB:  PubMed          Journal:  Brain Res        ISSN: 0006-8993            Impact factor:   3.252


  1 in total

1.  SAST-GCN: Segmentation Adaptive Spatial Temporal-Graph Convolutional Network for P3-Based Video Target Detection.

Authors:  Runnan Lu; Ying Zeng; Rongkai Zhang; Bin Yan; Li Tong
Journal:  Front Neurosci       Date:  2022-06-02       Impact factor: 5.152

  1 in total

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