Rather than considering the disentanglement of multicomponent nonstationary signals as a time-frequency post-processing, a possibility is to first decompose the observation into modes that are amenable to some further demodulations. In this spirit, this chapter reviews a technique that has recently gained popularity, namely “Empirical Mode Decomposition” and the associated “Hilbert-Huang Transform.” The rationale of those data-driven methods is presented, as well as their actual implementation, with a brief discussion of pros and cons with respect to more conventional time-frequency analysis.