Estimating Brain Connectivity with Varying-Length Time Lags Using a Recurrent Neural Network
We present a novel Granger causality estimator based on recurrent neural networks (RNNs) to deal with the multivariate brain connectivity detection problem. The varying-length propagation delay between brain signals poses a challenge for classical approaches using fixed time lags. Our method takes time series signals with arbitrary length of transmission time lags and learns the information flow from the data with RNNs. The proposed method works well on varying-length time lags and even on very long transmission delays in electroencephalography (iEEG) signals, and is promising to serve as a robust brain connectivity analysis tool in clinical applications.