Brainwave´s energy feature extraction using wavelet transform

Brainwaves has an important security and communication implication. Nevertheless, the reliability of the methods for discrimination between genuine and imposter is still in infancy. Efforts to enhance the reliability have exact identification using EEG. As EEG is non-stationary signal, the use of the joint time-frequency feature may be yield more reliable results. In this paper we have used cerebral region of central channel of brain waves named as CZ and used three wavelet decomposition functions Symlet, Daubechies and Coifet to investigate the potential of energy features such as Recoursing Energy Efficient (REE), Logarithmic REE and Absolute LREE for subject differentiation.