Analyzing Brain Connectivity in the Mutual Regulation of Emotion–Movement Using Bidirectional Granger Causality - Li, Ting; Li, Guoqi; Xue, Tao; Zhang, Jinhua (Frontiers in Neuroscience, 2020)
Body language and movement are important media of emotional expression. There is an interactive physiological relationship between emotion and movement. Thus, we hypothesized that the emotional cortex interacts with the motor cortex during the mutual regulation of emotion and movement and that this interaction can be revealed by brain connectivity analysis based on electroencephalogram (EEG) signal processing. Considering that EEG signals show complex dependencies with the special state of emotion-movement regulation, we proposed a brain connectivity analysis method : bi-directional long short-term memory Granger causality (bi-LSTM-GC) based on bi-directional LSTM recurrent neural networks (RNNs) and GC estimation. We then compared the accuracy of bi-LSTM-GC with other connectivity estimation algorithms derived from the principle of unidirectional dependency. The bi-LSTM-GC showed comparable results in the tests of multivariate linear and non-linear signals containing varying lag lengths. In the test of multivariate non-linear signals containing bi-directional temporal dependency, the proposed method was more robust than the comparator methods. In the paradigm of emotion-movement regulation, the detected directional dependencies of EEG signals were mainly distributed from the frontal to the central region and from the prefrontal to the central-parietal.