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Deciphering brain network topology can enhance the depth of neuroscientificknowledge and facilitate the development of neural engineering methods.Effective connectivity, a measure of brain network dynamics, is particularlyuseful for investigating the directional influences among different brainregions. In this study, we introduce a novel brain causal inference model namedInfoFlowNet, which leverages the self-attention mechanism to captureassociations among electroencephalogram (EEG) time series. The proposed methodestimates the magnitude of directional information flow (dIF) among EEGprocesses by measuring the loss of model inference resulting from the shufflingof the time order of the original time series. To evaluate the feasibility ofInfoFlowNet, we conducted experiments using a synthetic time series and two EEGdatasets. The results demonstrate that InfoFlowNet can extract time-varyingcausal relationships among processes, reflected in the fluctuation of dIFvalues. Compared with the Granger causality model and temporal causal discoveryframework, InfoFlowNet can identify more significant causal edges underlyingEEG processes while maintaining an acceptable computation time. Our workdemonstrates the potential of InfoFlowNet for analyzing effective connectivityin EEG data. The findings highlight the importance of effective connectivity inunderstanding the complex dynamics of the brain network.

InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity