TY - GEN
T1 - Using Markov Properties of ECoG Signals to Infer Neuron Connectivity
AU - Murin, Yonathan
AU - Goldsmith, Andrea
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85062942206
U2 - 10.1109/ACSSC.2018.8645499
DO - 10.1109/ACSSC.2018.8645499
M3 - Conference contribution
AN - SCOPUS:85062942206
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 671
EP - 675
BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Y2 - 28 October 2018 through 31 October 2018
ER -