  We present a new event trigger generator based on the Hilbert-Huang
transform, named EtaGen ($\eta$Gen). It decomposes a time-series data into
several adaptive modes without imposing a priori bases on the data. The
adaptive modes are used to find transients (excesses) in the background noises.
A clustering algorithm is used to gather excesses corresponding to a single
event and to reconstruct its waveform. The performance of EtaGen is evaluated
by how many injections in the LIGO simulated data are found. EtaGen is viable
as an event trigger generator when compared directly with the performance of
Omicron, which is currently the best event trigger generator used in the LIGO
Scientific Collaboration and Virgo Collaboration.
