Mining Temporal Dynamics With Support Vector Machine for Predicting the Neural Fate of Target in Attentional Blink - Yao, Yuan; Wu, Yunying; Xu, Tianyong; Chen, Feiyan (Frontiers in Systems Neuroscience, 2021)
Our brain does not mechanically process incoming stimuli; on the contrary, 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, rarely did they quantitatively explored the neural mechanism. By utilizing the attentional blink (AB) effect, we aim to identify the neural mechanism of brain states preceding T2 and predict its behavioral performance. The inter-area phase synchronization and its role in prediction was explored by using PLV (phase locking value) and SVM (support vector machine) classier. Our results showed that the phase coupling in alpha and beta frequency band 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, supporting the attention control theories.