Conclusion

Abstract

Wavelet analysis remains a somewhat exotic method in contemporary neuroscience and neurophysiology. It seems that new mathematical or experimental methods, despite all their benefits and technological advantages, need time to become accepted as a convenient tool for routine applications. It is particularly noticeable in the clinical and biological sciences, where novel mathematical tools must undergo a thorough examination, adaptation, and verification, and only then can they be accepted for practical use. In this context, it should be emphasized that wavelet analysis is suitable for time–frequency analysis of neurophysiological signals, and can also be incorporated into more complex algorithms for experimental data processing that increase the efficacy of data analysis in neurophysiological studies. We believe that the wavelet-based analysis will naturally evolve into a family of standard methods for signal processing in biology and medicine. This does not mean replacement of the conventional by new techniques, but improvement of existing approaches to make wavelet analysis more widely applicable in experimental neuroscience.