The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China.
The Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 610000, China.
Brain Res. 2021 Aug 15;1765:147502. doi: 10.1016/j.brainres.2021.147502. Epub 2021 Apr 24.
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.
在动态视频目标检测任务中，由于视觉场景的动态性和视觉信息的不确定性，干扰物可能会突然出现，这强烈影响了参与者的注意力和目标检测性能。此外，用于解释动态干扰物处理的神经机制尚不清楚，这使得基于 EEG 的视频目标检测难以进行补偿。在这里，使用源定位方法重建了具有高时空分辨率的皮质活动。确定了不同认知阶段中重要脑区之间的时变网络，包括信息整合、决策和执行，以研究动态干扰物处理的神经机制。实验结果表明，动态干扰物会引起类似 P3 的成分。此外，在视频目标检测过程中，两个半球之间存在明显的不对称性。具体来说，在信息整合阶段，动态干扰物引起的大脑反应较弱，且更集中在左半球；与存在干扰物的准备相关的左额上回活动至关重要，而在决策阶段，注意力网络和初级视觉网络（尤其是左视觉通路）对动态目标更为活跃。这些发现为设计有效的基于 EEG 的动态视频目标检测模型提供了指导。
