The project introduces a novel benchmark for assessing methods that derive effective connectivity networks from brain imaging data. By employing a classification approach and explainable machine learning models, this framework enables a rigorous comparison of different techniques, including reservoir computing and Granger causality, ultimately contributing to a deeper understanding of brain function.
Home Research Research Topics A Machine Learning Benchmark for Effective Connectivity Network Derivation