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. 2020 May 6:14:369.
doi: 10.3389/fnins.2020.00369. eCollection 2020.

Analyzing Brain Connectivity in the Mutual Regulation of Emotion-Movement Using Bidirectional Granger Causality

Ting Li  1   2 , Guoqi Li  3 , Tao Xue  1   2 , Jinhua Zhang  4
Affiliations

Analyzing Brain Connectivity in the Mutual Regulation of Emotion-Movement Using Bidirectional Granger Causality

Ting Li et al. Front Neurosci. .

Abstract

Body language and movement are important media of emotional expression. There is an interactive physiological relationship between emotion and movement. Thus, we hypothesize that the emotional cortex interacts with the motor cortex during the mutual regulation of emotion and movement. And this interaction can be revealed by brain connectivity analysis based on electroencephalogram (EEG) signal processing. We proposed a brain connectivity analysis method: bidirectional long short-term memory Granger causality (bi-LSTM-GC). The theoretical basis of the proposed method was Granger causality estimation using a bidirectional LSTM recurrent neural network (RNN) for solving nonlinear parameters. Then, we compared the accuracy of the bi-LSTM-GC with other unidirectional connectivity methods. The results demonstrated that the information interaction existed among multiple brain regions (EEG 10-20 system) in the paradigm of emotion-movement regulation. The detected directional dependencies in EEG signals were mainly distributed from the frontal to the central region and from the prefrontal to the central-parietal.

Keywords: EEG; Granger causality (GC); bi-LSTM network; brain connectivity analysis; emotion; movement.

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Figures

FIGURE 1
FIGURE 1
Videos on eight emotion themes.
FIGURE 2
FIGURE 2
Layout of the 34 electrodes.
FIGURE 3
FIGURE 3
The algorithm structure of the bidirectional long short-term memory Granger causality (bi-LSTM-GC).
FIGURE 4
FIGURE 4
Dependency of the simulation signals generated by the three models. Model A is a multivariate linear model. Model B is a multivariate nonlinear model with varying lag lengths. Model C is a multivariate nonlinear model with varying lag lengths and bidirectional time dependency.
FIGURE 5
FIGURE 5
Results derived from three simulation models are illustrated. (1) Model A: linear simulations, (2) model B: nonlinear simulations with varying lag lengths, (3) model C: nonlinear simulations with bidirectional dependency and varying lag lengths. The colored matrices represent the distribution of the overall directional dependencies. The term “Mij” refers to the directional dependency from sequence i to sequence j. Different colors represent dependence intensity, and lighter colors indicate stronger dependencies. All the results are averaged over 10 trials, and self-dependencies are not included.
FIGURE 6
FIGURE 6
Dependencies among different brain regions.
See this image and copyright information in PMC

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