Subband correlation for EEG data in the dual tree complex wavelet transform domain for the detection of epilepsy and seizure

In this paper, a comprehensive analysis of electroencephalogram (EEG) signals is carried out in the dual tree complex wavelet transform domain using a publicly available EEG database. It is shown that maximum cross-correlation among the sub-bands along with the absolute values of the corresponding correlation coefficient and co-variance can be effective in distinguishing EEG signals such as seizure and non-seizure. Thus, these quantities may be used to characterize EEG signals to realize the underlying diverse process of EEG recordings and help the researchers in developing improved classifiers for the detection of epilepsy and seizure.