Classification-Based Causality Detection in Time Series
Conference paper
First Online:
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.
References
- 1.Baccalà, L.A., Sameshima, K.: Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84(6), 463–474 (2001). http://view.ncbi.nlm.nih.gov/pubmed/11417058 CrossRefzbMATHGoogle Scholar
- 2.Baccalà, L.A., Sameshima, K., Ballester, G., Do Valle, A.C., Timo-Iaria, C.: Studying the interaction between brain structures via directed coherence and granger causality. Appl. Signal Process. 5, 40–48 (1998). http://www.lcs.poli.usp.br/~baccala/pdc/papers/asp.pdf
- 3.Brookes, M.J., Woolrich, M.W., Barnes, G.R.: Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. NeuroImage 63(2), 910–920 (2012). http://view.ncbi.nlm.nih.gov/pubmed/22484306 CrossRefGoogle Scholar
- 4.Butler, S.R., Glass, A.: Asymmetries in the electroencephalogram associated with cerebral dominance. Electroencephalogr. Clin. Neurophysiol. 36(5), 481–491 (1974). http://view.ncbi.nlm.nih.gov/pubmed/4135345 CrossRefGoogle Scholar
- 5.Faes, L., Erla, S., Nollo, G.: Measuring connectivity in linear multivariate processes: definitions, interpretation, and practical analysis. Comput. Math. Methods Med. 2012, 1–18 (2012). doi: 10.1155/2012/140513 MathSciNetzbMATHGoogle Scholar
- 6.Freiwald, W.A., Valdes, P., Bosch, J., Biscay, R., Jimenez, J.C., Rodriguez, L.M., Rodriguez, V., Kreiter, A.K., Singer, W.: Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex. J. Neurosci. Methods 94(1), 105–119 (1999). http://view.ncbi.nlm.nih.gov/pubmed/10638819 CrossRefGoogle Scholar
- 7.Friston, K.J.: Functional and effective connectivity: a review. Brain Connectivity 1(1), 13–36 (2011). doi: 10.1089/brain.2011.0008 MathSciNetCrossRefGoogle Scholar
- 8.Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969). doi: 10.2307/1912791 MathSciNetCrossRefGoogle Scholar
- 9.Hlavackovaschindler, K., Palus, M., Vejmelka, M., Bhattacharya, J.: Causality detection based on information-theoretic approaches in time series analysis. Phys. Rep. 441(1), 1–46 (2007). doi: 10.1016/j.physrep.2006.12.004 CrossRefGoogle Scholar
- 10.Horwitz, B.: The elusive concept of brain connectivity. NeuroImage 19(2 Pt 1), 466–470 (2003). http://view.ncbi.nlm.nih.gov/pubmed/12814595 CrossRefGoogle Scholar
- 11.Kamiński, M., Ding, M., Truccolo, W.A., Bressler, S.L.: Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern. 85(2), 145–157 (2001). http://view.ncbi.nlm.nih.gov/pubmed/11508777 CrossRefzbMATHGoogle Scholar
- 12.Kaminski, M.J., Blinowska, K.J.: A new method of the description of the information flow in the brain structures. Biol. Cybern. 65(3), 203–210 (1991). doi: 10.1007/bf00198091 CrossRefzbMATHGoogle Scholar
- 13.Papana, A., Kugiumtzis, D., Larsson, P.G.: Reducing the bias of causality measures. Phys. Rev. E 83(3) (2011). http://dx.doi.org/10.1103/physreve.83.036207
- 14.Pereda, E., Quiroga, R.Q.Q., Bhattacharya, J.: Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 77(1–2), 1–37 (2005). doi: 10.1016/j.pneurobio.2005.10.003 CrossRefGoogle Scholar
- 15.Sakkalis, V.: Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comput. Biol. Med. 41(12), 1110–1117 (2011). doi: 10.1016/j.compbiomed.2011.06.020 CrossRefGoogle Scholar
- 16.Winterhalder, M., Schelter, B., Hesse, W., Schwab, K., Leistritz, L., Klan, D., Bauer, R., Timmer, J., Witte, H.: Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems. Sig. Process. 85(11), 2137–2160 (2005). doi: 10.1016/j.sigpro.2005.07.011 CrossRefzbMATHGoogle Scholar
Copyright information
© Springer International Publishing AG 2016