@inproceedings{106d108fbad94588ad88153bbd3f1aa4,
title = "Using Markov Properties of ECoG Signals to Infer Neuron Connectivity",
abstract = "Quantifying the causal associations between Electrocorticography (ECoG) recordings has multiple applications in neuroscience. In this work we study the Markov properties of ECoG recordings and their impact on estimating causal influences between these signals. We show that accurate estimation of the causal influence requires knowledge of the Markov order of the considered recordings. Since in general the Markov orders are unknown, they must be estimated from the data before the causal associations are estimated. To address this challenge we propose a data-driven method for estimating these Markov orders.",
author = "Yonathan Murin and Andrea Goldsmith",
note = "Funding Information: The work was partly supported by the NSF Center for Science of Information (CSoI) under grant NSF-CCF-0939370. Publisher Copyright: {\textcopyright} 2018 IEEE.; 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 ; Conference date: 28-10-2018 Through 31-10-2018",
year = "2019",
month = feb,
day = "19",
doi = "10.1109/ACSSC.2018.8645499",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "671--675",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",
address = "United States",
}