Non-uniform state space reconstruction and coupling detection

TL;DR

This paper introduces a flexible method for reconstructing state spaces from multiple time series, enabling improved detection of information transfer and causality in coupled systems, with applications to epileptic EEG analysis.

Contribution

The paper presents a novel adaptive embedding scheme based on information measures for analyzing coupled systems and detecting information transfer.

Findings

Effective detection of information flow in EEG data

Versatile embedding scheme adaptable for various analyses

Application to epileptic brain activity data

Abstract

We investigate the state space reconstruction from multiple time series derived from continuous and discrete systems and propose a method for building embedding vectors progressively using information measure criteria regarding past, current and future states. The embedding scheme can be adapted for different purposes, such as mixed modelling, cross-prediction and Granger causality. In particular we apply this method in order to detect and evaluate information transfer in coupled systems. As a practical application, we investigate in records of scalp epileptic EEG the information flow across brain areas.