ABSTRACT
Thus chapter presents a new method for analyzing non-stationary signals in human motor behavior: the wavelet transform (WT). It focuses on the cross-wavelet transform (CWT) that gives information about interactions between two signals. The CWT's method is based on the time-frequency plane. The interest in performing the analysis in the time-frequency plane is justified by the fact that typically, for non-stationary signals, interpretation of temporal analysis results without a frequency component is difficult, as interpretation of frequency analysis results is difficult without any temporal information. Moreover, the wavelet transform provides a flexible time-scale window that narrows when focusing on small-scale features and widens on large-scale features. The most popular wavelet is Morlet wavelet, a complex mother function that allows any analysis of the interaction between two signals. The results obtained with such a wavelet are represented in two spectrograms.
