A Wavelet-based Energetic Approach for the Analysis of Electroencephalogram

Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. EEG signals, like many biomedical signals, are highly non-stationary by their nature. Wavelet analysis has found a prominent position in the investigation of biomedical signals for its ability to analyze such signals, in particular EEG signals. Wavelet transform is capable of separating the signal energy among different frequency bands (i.e., different scales), achieving a good compromise between temporal and frequency resolution. The present study is an attempt to better understand the mechanisms causing epilepsy and accurate prediction of the occurrence of seizures. In the present paper we identify typical patterns of energy redistribution before and during a seizure using multi-resolution wavelet analysis.