Recognizing brain motor imagery activities by identifying chaos properties of oxy-hemoglobin dynamics time series

In recent years, functional near-infrared spectroscopy (NIRS) has been introduced as a new neuroimaging modality with which to conduct functional brain-imaging studies. With its advanced features, NIRS signal processing has become a very attractive field in computational science. This work explores nonlinear physical aspects of cerebral hemodynamic changes over the time series of NIRS. Detecting the presence of chaos in a dynamical system is an important problem in studying the irregular or chaotic motion that is generated by nonlinear systems whose dynamical laws uniquely determine the time of evolution of a state of the system. The strategy results directly from the definition of the largest Lyapunov exponent. The Lyapunov exponents quantify the exponential divergence of initially close state–space trajectories and estimate the amount of chaos in a system. The method is an application of the Rosenstein algorithm, an efficient method for calculating the largest Lyapunov exponent from an experimental time series. In the present paper, the authors focus mainly on the detection of chaos characteristics of the time series associated to hemoglobin dynamics. Furthermore, the chaos parameters obtained can be used to identify the active state period of the human brain.