Number of EEG signal components estimated using the short-term Renyi entropy

Multichannel electroencephalogram (EEG) signals are known to be highly non-stationary and often multi-component. A new method for its complexity, in terms of number of signal components extracted from its time-frequency distributions, has been proposed in this paper. Exploiting its spectral energy variation with time, the joint time-frequency distribution approach was upgraded by the modification of Rényi entropy, called short-term Rényi entropy, and applied to multichannel EEG signal analysis resulting in novel algorithm for its complexity detection. Number of EEG signals components obtained for various EEG signals was shown to provide useful information concerning brain activity at each electrode location, which may further be used to detect the brain activity abnormalities for patients with limb movement difficulties.