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 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.