A Comparison of Effective Connectivity Methods Using Different Performance Metrics

Su-Kyoung Kim; Suraj Kumar Sanga; Elsa Andrea Kirchner

In: Proceedings of the 6th International IEEE EMBS Conference on Neural Engineering. International IEEE/EMBS Conference on Neural Engineering (NER-2013), November 6-8, San Diego, CA, USA, Pages 823-826, 11/2013.


Different analysis methods have been developed to determine brain connectivity patterns. To select suitable methods depending on application contexts, it is essential to evaluate different methods using suitable performance metrics. We propose three application-oriented metrics which enable to measure multivariate causality qualitatively. Using the proposed metrics, the most used analysis methods (Directed Transfer Function, Partial Directed Coherence, Granger-Geweke Causality) are compared on synthetic electroencephalographic data with a predefined causality structure. Furthermore, the performances obtained by using all metrics are evaluated. The results allow us to select the most stable analysis method and the optimal metric by estimating the similarity between the performance obtained by using each metric and the graphically displayed predicted network.


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130802_A_Comparison_of_Effective_Connectivity_Methods_Using_Different_Performance_Metrics_NER_Kim.pdf (pdf, 301 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence