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使用支持向量机挖掘时间动态以预测注意瞬脱中目标的神经命运。

Mining Temporal Dynamics With Support Vector Machine for Predicting the Neural Fate of Target in Attentional Blink.

作者信息

Yao Yuan, Wu Yunying, Xu Tianyong, Chen Feiyan

机构信息

Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou, China.

Department of Education, Suzhou University of Science and Technology, Suzhou, China.

出版信息

Front Syst Neurosci. 2021 Oct 29;15:734660. doi: 10.3389/fnsys.2021.734660. eCollection 2021.

Abstract

Our brains do not mechanically process incoming stimuli; in contrast, the physiological state of the brain preceding stimuli has substantial consequences for subsequent behavior and neural processing. Although previous studies have acknowledged the importance of this top-down process, it was only recently that a growing interest was gained in exploring the underlying neural mechanism quantitatively. By utilizing the attentional blink (AB) effect, this study is aimed to identify the neural mechanism of brain states preceding T2 and predict its behavioral performance. Interarea phase synchronization and its role in prediction were explored using the phase-locking value and support vector machine classifiers. Our results showed that the phase coupling in alpha and beta frequency bands pre-T1 and during the T1-T2 interval could predict the detection of T2 in lag 3 with high accuracy. These findings indicated the important role of brain state before stimuli appear in predicting the behavioral performance in AB, thus, supporting the attention control theories.

摘要

我们的大脑并非机械地处理传入的刺激;相反,刺激出现之前大脑的生理状态会对随后的行为和神经处理产生重大影响。尽管先前的研究已经认识到这种自上而下过程的重要性,但直到最近,人们才越来越有兴趣定量地探索其潜在的神经机制。通过利用注意瞬脱(AB)效应,本研究旨在确定T2之前大脑状态的神经机制并预测其行为表现。使用锁相值和支持向量机分类器探索了区域间相位同步及其在预测中的作用。我们的结果表明,T1之前以及T1-T2间隔期间的α和β频段的相位耦合能够高精度地预测T2在滞后3时的检测。这些发现表明,刺激出现之前的大脑状态在预测AB中的行为表现方面具有重要作用,从而支持了注意力控制理论。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e07/8589014/932161aad618/fnsys-15-734660-g001.jpg

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