Nonlinear Dynamics Techniques for the Detection of the Brain Areas Using MER Signals

A methodology for identifying brain areas from the brain MER signals (microelectrode recordings) is presented, which is based on a nonlinear feature set. We propose nonlinear dynamics measures such as correlation dimension, Hurst exponent and the largest Lyapunov exponent to characterize the dynamic structure. The MER records belong to the Polytechnical University of Valencia, 24 records for each zone (black substance, thalamus, subthalamus nucleus and uncertain area). The detection of each area using characteristics derived from complexity analysis was obtained through a classifier (support vector machine). The joint information between areas is remarkable and the best accuracy result was 93.75%. The nonlinear dynamics techniques help to discriminate the four brain areas considered, since they take into account the intrinsic dynamics of the signals and the structures analysis based on the multivariate statistical procedures is an important step in the data preprocessing.