Information Geometry Approach to Analyzing Simulated EEG Signals of Alzheimer's Disease Patients and Healthy Control Subjects

In this work, we explore information geometry theoretic approach to analyzing EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer’s Disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal’s instantaneous influence on another signal’s information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they change their eyes’ open/closed status. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. In particular, a prominent distinction is that, when healthy subjects open their eyes, there is a directional change in net causal connectivities among brain regions (that generate EEG signals modeled by stochastic nonlinear coupled oscillators), as measured by net causal information rates, whereas this directional change in net causality does not present when AD patients open their eyes. Since these information geometry theoretic measures can be applied to experimental EEG signals in a modelfree manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and diagnosis of neurological disorders such as Alzheimer’s Disease.