Mutidimensional analysis of EEG features using advanced spectral estimates for diagnosis accuracy

Abstract

Electroencephalogram (EEG) is a source of interesting information if one is able to extract them according to appropriate techniques. The conditions of individual under EEG test is a key issue. In general, EEG feature extraction can be associated to other information like Electrocardiogram (ECG), ergospirometry and electromyogram (EMG). However, in some cases, a multidimensional representation is used; bispectrum is an example of such a representation. HOS (high order statistics), for instance, include the bispectrum and the trispectrum (third and fourth order statistics, respectively). Advanced estimate spectral analysis can reveal new information encompassed in EEG signals. That is the reason the author propose an algorithm based on DSD (Decimated Signal Diagonalization) that is able of processing exponentially dumped signals like those that regard EEG features. The version proposed here is a multidimensional one.