Dissimilarity of Graph Invariant Features from EEG Phase-space Analysis
Electroencephalogram (EEG) data has been used in a variety of linear and nonlinear time series analysis techniques for predicting epileptic seizures. We examine phase-space dissimilarity measures for forewarning of seizure events based on time-delay embedding and state space recreation of the underlying brain dynamics.
Given novel states which form graph nodes and dynamical linkages between states which form graph edges, we use graph dissimilarity to detect dynamical phase shifts which indicate the onset of epileptic events. In this paper, we report on observed trends and characteristics of graphs based on event and nonevent data from human EEG observations, and extend previous work focused on node and link dissimilarity by analyzing other graph properties as well. Our analysis includes measured properties and dissimilarity features that influence forewarning prediction accuracy.