Classification-Based Causality Detection in Time Series
- Cite this paper as:
- Benozzo D., Olivetti E., Avesani P. (2016) Classification-Based Causality Detection in Time Series. In: Rish I., Langs G., Wehbe L., Cecchi G., Chang K., Murphy B. (eds) Machine Learning and Interpretation in Neuroimaging. MLINI 2014, MLINI 2013. Lecture Notes in Computer Science, vol 9444. Springer, Cham
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.