Multiple window time-frequency distribution and coherence of EEG using Slepian sequences and Hermite functions

Multiple window (MW) time-frequency analysis (TFA) is a newly developed tec hnique to estimate a time-varying spectrum for random nonstationary signals with low bias and variance. In this paper, we describe the application of MW-TFA techniques to electroencephalogram (EEG) and compare the results wit h those of the conventional spectrogram. We find that the MW-TFA provide us with not only low bias and variance time-frequency (TF) distribution for E EG but also TF coherence estimation between a single realization of EEG rec orded from two sites. We also compare the performance of the MW-TFA using t wo sets of windows, Slepian sequences, and Hermite functions. If care is ta ken in matching the two windows, we find no noticeable difference in the re sulting TF representations.