%0 Journal Article
%T Nonlinear analysis of EEG signals at different mental states
%A Kannathal Natarajan
%A Rajendra Acharya U
%A Fadhilah Alias
%A Thelma Tiboleng
%A Sadasivan K Puthusserypady
%J BioMedical Engineering OnLine
%D 2004
%I BioMed Central
%R 10.1186/1475-925x-3-7
%X In this work, nonlinear parameters like Correlation Dimension (CD), Largest Lyapunov Exponent (LLE), Hurst Exponent (H) and Approximate Entropy (ApEn) are evaluated from the EEG signals under different mental states.The results obtained show that EEG to become less complex relative to the normal state with a confidence level of more than 85% due to stimulation.It is found that the measures are significantly lower when the subjects are under sound or reflexologic stimulation as compared to the normal state. The dimension increases with the degree of the cognitive activity. This suggests that when the subjects are under sound or reflexologic stimuli, the number of parallel functional processes active in the brain is less and the brain goes to a more relaxed stateThe electrical activity of a brain measured by Electroencephalogram (EEG) exhibits complex behavior with nonlinear dynamic properties. This behavior takes the form of EEG patterns with different complexities. Considering this, the nonlinear dynamics theory may be a better approach than traditional linear methods in characterizing the intrinsic nature of EEG. The study of nonlinear dynamics and characterization can contribute to the understanding of the EEG dynamics and underlying brain processes and search for its physiological significance. The literature on the study of the application of the nonlinear dynamics theory to analyze physiological signals, shows that nonlinear approaches were used for analysis of heart rate, nerve activity, renal blood flow, arterial pressure, EEG and respiratory signals [1,2].The importance of the biological time series analysis, which exhibits typically complex dynamics, has long been recognized in the area of nonlinear analysis. Several features of these approaches have been proposed to detect the hidden important dynamical properties of the physiological phenomenon. The nonlinear dynamical techniques are based on the concept of chaos and it has been applied to many areas incl
%U http://www.biomedical-engineering-online.com/content/3/1/7