  Deciphering brain network topology can enhance the depth of neuroscientific
knowledge and facilitate the development of neural engineering methods.
Effective connectivity, a measure of brain network dynamics, is particularly
useful for investigating the directional influences among different brain
regions. In this study, we introduce a novel brain causal inference model named
InfoFlowNet, which leverages the self-attention mechanism to capture
associations among electroencephalogram (EEG) time series. The proposed method
estimates the magnitude of directional information flow (dIF) among EEG
processes by measuring the loss of model inference resulting from the shuffling
of the time order of the original time series. To evaluate the feasibility of
InfoFlowNet, we conducted experiments using a synthetic time series and two EEG
datasets. The results demonstrate that InfoFlowNet can extract time-varying
causal relationships among processes, reflected in the fluctuation of dIF
values. Compared with the Granger causality model and temporal causal discovery
framework, InfoFlowNet can identify more significant causal edges underlying
EEG processes while maintaining an acceptable computation time. Our work
demonstrates the potential of InfoFlowNet for analyzing effective connectivity
in EEG data. The findings highlight the importance of effective connectivity in
understanding the complex dynamics of the brain network.
