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.
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Notes
- 1.
http://www.biomag2014.org/competition.shtml, see “Challenge 2: Causality Challenge”.
- 2.
The diagonal is not relevant since by definition the time series are autoregressive.
- 3.
Excluding the trial-specific parameter \(\gamma \) which was randomly uniformly generated for each trail.
- 4.
- 5.
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Benozzo, D., Olivetti, E., Avesani, P. (2016). Classification-Based Causality Detection in Time Series. In: Rish, I., Langs, G., Wehbe, L., Cecchi, G., Chang, Km., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. MLINI MLINI 2013 2014. Lecture Notes in Computer Science(), vol 9444. Springer, Cham. https://doi.org/10.1007/978-3-319-45174-9_9
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