Assessing the strength of directed influences among neural signals : An approach to noisy data

Sommerlade, LindaThiel, MarcoMader, MalenkaMader, WolfgangTimmer, JensPlatt, BettinaSchelter, Bjoern2015-03-182015-03-182015-01-15Sommerlade, L, Thiel, M, Mader, M, Mader, W, Timmer, J, Platt, B & Schelter, B 2015, 'Assessing the strength of directed influences among neural signals : An approach to noisy data', Journal of Neuroscience Methods, vol. 239, pp. 47-64. https://doi.org/10.1016/j.jneumeth.2014.09.0070165-0270ORCID: /0000-0002-7852-0749/work/76976860http://hdl.handle.net/2164/4326Acknowledgements This work was supported by the German Science Foundation (Ti315/4-2), the German Federal Ministry of Education and Research (BMBF grant 01GQ0420), and the Excellence Initiative of the German Federal and State Governments. B.S. is indebted to the Kosterlitz Centre for the financial support of this research project.18445824engGranger-causalityObservational noiseStatisticsExpectation-maximisation algorithmKalman filterIncomplete data likelihoodAnalytical covariance matrixMultivariate time-seriesGranger causalityMaximum-likelihoodLinear-dependenceInformation-flowCoherenceEEGFeedbackIntervalAlgorithmRC0321 Neuroscience. Biological psychiatry. NeuropsychiatrySupplementary DataRC0321Assessing the strength of directed influences among neural signals : An approach to noisy dataJournal article10.1016/j.jneumeth.2014.09.007239