Shaozhe Feng

Qiushi Academy for Advanced Studies, and also the College of Computer Science and Technology, Zhejiang University, Hangzhou, China


Contributions

  • Estimating Brain Connectivity with Varying-Length Time Lags Using a Recurrent Neural Network
    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.

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