This paper discusses a real time stimulus timing detection for a Brain-Machine-Interface (BMI). We present a low complexity detector for detecting the stimulus onset time from real multichannel, multi-unit electro-physiological data, recorded from a brainstem area called Pontine Nucleus (PN). The detector contains a novel pre-processing block, which takes advantage of the high coherence between different channels during response, in order to enhance the Signal-to- Noise Ratio (SNR), as well as to achieve higher detection rates. An intuitive effective method for fusion and combination of different channels based on spike counts is used. A full detailed description of the algorithm blocks is presented, along with its optimized parameters according to real data performance evaluation.