Biomedical Systems Modeling and Diagnostics

The main research thrust of this research cluster is bio-signal processing and discriminant analysis of the Brain electroencephalography (EEG) Signal. Since recording of the EEG signal is non-invasive and safe, its analysis is considered to be a potential tool that may aid in the diagnosis of brain disorders and injuries including Epilepsy, Alzheimer’s disease, and mild Traumatic Brain Injury. In this research, a variety of techniques including spectral analysis, discrete and continuous wavelet transform methods, and stochastic nonlinear dynamic analysis are employed to analyze EEG signals recorded from human brain. Past results include identification of a variety of EEG discriminants for Alzheimer’s disease using discrete and continuous wavelet transforms. In addition, we have developed distinct nonlinear stochastic oscillator models of EEG recording under different brain conditions for Alzheimer’s disease patients and healthy normal subjects. Current objective is to extent these approaches to monitor brain health and aid in the diagnosis of mild Traumatic Brain Injury or concussion. In particular, the focus will be on nonlinear stochastic oscillator model of the EEG signal based on mutli-time scale analysis which can provide a unique tool for evaluation of concussion progression

Absolute Values of Coefficients
COEFS