Looks very promising and tempting for time-frequency analysis - but because of the algorithmic nature of EMD, the conditions for a signal to be an IMF are rarely, if ever, met. The definitions of the IMF are far too stringent and the process used to obtain them, the EMD, is far too subjective. Some researchers have gone so far as to say that there might be an unseen paradox between the two arguments.
Thus, while a rigorous treatment of the EMD is welcome, a survey of different versions of the EMD algorithm and stoppage criteria would be more pragmatic. We need to investigate the possibility of developing a yardstick to compare different stoppage conditions, subject, of course, to the respective application. Within the purview of a certain application domain - time-series analysis, machine learning, regression, etc - we should be able to pit one stoppage criterion against another and see if we can comment on which is better.
Here's a little demonstration of a very naive EMD algorithm, in the sense that it takes very moderate views on the definitions of the IMF. The input is a part of a OAE dataset - the distortion product amplitude of about 1848 audiometric tests.