Comparison of synchrosqueezing transform to alternative methods for time-frequency analysis of TMS-evoked EEG oscillations

Background: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has become a powerful tool to assess cortical properties, such as cortical oscillation. An accurate and robust time-frequency analysis tool with high resolution would facilitate the understanding of TMS-evoked oscillations. Methods: The synchrosqueezing transform (SST), the Hilbert-Huang transform (HHT) and the Morlet wavelet transform (MWT) were used to analyze TMS-evoked oscillations. Firstly, we generated simulation data, and compared the performance of three methods to analyze the time-frequency characteristics of simulation data; then, we collected TMS-EEG data from normal people (NOR), patients with minimally conscious state (MCS) and vegetative state (VS). SST was used to analysis time-frequency characteristics of TMS-evoked oscillations in different states of consciousness. In addition, relative power (RP) and spectral entropy (SeEn) were calculated based on SST results. Results: Simulation results showed that SST detected rhythm characteristics of instantaneous signal and background signal more completely. Results of NOR group showed that SST could more accurately detect TMS-evoked oscillations with high frequency resolution than HHT and MWT. The main frequency of TMS-evoked oscillations for DOC patients (MCS: 11.73 +/- 1.94 Hz; VS: 2.06 +/- 0.25 Hz) was different from that of NOR (22.79 +/- 1.42 Hz) based on SST. RP and SeEn further verified the differences of the main frequency of TMS-evoked oscillations between NOR and DOC patients. Conclusions: The results suggest that SST is a robust analytical method and outperforms HHT and MWT in studying TMS-evoked oscillations. The main frequency of TMS-evoked oscillations of DOC was lower than that of NOR.