EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
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The paper is devoted to a novel method for detecting the back-loop regulation of coupling and decoupling between cortical neurones from electroencephalogram (EEG) signals. In this method, a linear time-scale quantifier of the multivariate relationship between simultaneously observed time series takes advantage of unique properties of complex wavelets such as shift invariance, substantially reduced aliasing and nonoscillating magnitude. The quantifier provides correlation between amplitude of complex wavelet coefficients for different frequencies (controlled by the scale factor) at different times (controlled by the time shift). Biological interpretation of this measure is derived from a priori information about presence of inhibitory back-loop connections between cortical neurones in short distance while the long range excitatory connections do not appear to target or effectively excite inhibitory interneurons and analysis of sample pairs of EEG signals.