A single pulse of Transcranial Magnetic Stimulation (TMS) generates electroencephalogram (EEG) oscillations that are thought to reflect intrinsic properties of the stimulated cortical area and its fast interactions with other cortical areas. Thus, a tool to decompose TMS-evoked oscillations in the time-frequency domain on a millisecond timescale and on a broadband frequency range may help to understand information transfer across cortical oscillators. Some recent studies have employed algorithms based on the Wavelet Transform (WT) to study TMS-evoked EEG oscillations in healthy and pathological conditions. However, these methods do not allow to describe TMS-evoked EEG oscillations with high resolution in time and frequency domains simultaneously. Here, we first develop an algorithm based on Hilbert-Huang Transform (HHT) to compute statistically significant time-frequency spectra of TMS-evoked EEG oscillations on a single trial basis. Then, we compared the performances of the HHT-based algorithm with the WT-based one by applying both of them to a set of simulated signals. Finally, we applied both algorithms to real TMS-evoked potentials recorded in healthy or schizophrenic subjects. We found that the HHT-based algorithm outperforms the WT-based one in detecting the time onset of TMS-evoked oscillations in the classical EEG bands. These results suggest that the HHT-based algorithm may be used to study the communication between different cortical oscillators on a fine time scale.