Bilinear time}frequency distributions have been widely utilized in the analysis of nonstationary biomedical signals. A problem often arises where the time}frequency components with small-amplitude values cannot be displayed clearly. This problem results from a masking e!ect on these components caused by the presence of high-energy slow waves and sharp patterns in the input which produce large values in the time}frequency distribution. These large values often appear in the time}frequency plane as irregular patterns in the low-frequency range (due to slow waves), and as wide-band, impulsive components at certain points in time (due to sharp patterns). In this work we present an e!ective signal pre-processing method using a nonlinear operation on wavelet coe$cients. This method equalizes the energy of di!erent time}frequency components in the data so that the masking e!ect is greatly reduced, while the original time}frequency features of the input signal are preserved. Comparative experiments on electroencephalographic data with and without using this method have shown a clear improvement in the readability and sensitivity in bilinear time}frequency distributions.