MULTIRESOLUTION DECOMPOSITION OF NON-STATIONARY EEG SIGNALS: A PRELIMINARY STUDY

Abstract Wavelet representation is a recent development in the analysis of non-stationary signals. Its possibilities for use in the description of time-frequency characteristics of both transients in spontaneous EEG and time-varying rhythms in event related brain activity are explored here. By way of illustration, multiresolution decompositions of a wide variety of EEG transients are carried out in this work, including spike-and-waves, single spikes, sharp waves, blink artifacts, frontal intermittent rhythmic delta activity (FIRDA) and paroxysmal delta activity. Also, the application of the wavelet representation to study event related spectra perturbations is illustrated with data from psychophysical experiments on the perception of image motion. The results demonstrate the capabilities of the wavelet transform, as an alternative to the Fourier transform, for the representation and analysis of non-stationary EEG signals.