Classification-Based Causality Detection in Time Series

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9444)

Abstract

Brain effective connectivity aims to detect causal interactions between distinct brain units and it can be studied through the analysis of magneto/electroencephalography (M/EEG) signals. Methods to evaluate effective connectivity belong to the large body of literature related to detecting causal interactions between multivariate autoregressive (MAR) data, a field of signal processing. Here, we reformulate the problem of causality detection as a supervised learning task and we propose a classification-based approach for it. Our solution takes advantage of the MAR model by generating a labeled data set that contains trials of multivariate signals for each possible configuration of causal interactions. Through the definition of a proper feature space, a classifier is trained to identify the causality structure within each trial. As evidence of the efficacy of the proposed method, we report both the cross-validated results and the details of our submission to the causality detection competition of Biomag2014, where the method reached the 2nd place.

Keywords

Granger Causality Multivariate Time Series Causal Interaction Effective Connectivity Direct Transfer Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Danilo Benozzo
    • 1
    • 2
  • Emanuele Olivetti
    • 1
    • 2
  • Paolo Avesani
    • 1
    • 2
  1. 1.NeuroInformatics Laboratory (NILab)Bruno Kessler FoundationTrentoItaly
  2. 2.Center for Mind and Brain Sciences (CIMeC)University of TrentoTrentoItaly

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