Somatosensory evoked potential (SEP) testing has been widely applied to intraoperative spinal cord integrity monitoring, diagnosis of various neurological disorders, and nerve conduction velocity measurements. However, the SEP recorded using surface electrodes is buried in noises that are both electrical and biological in nature. Hence, the noninvasive measurements of these potentials suffer from very poor signal-to-noise ratios (SNR). Some means of signal processing is required to extract SEP signal from strong background noise. The most commonly used technique of SEP extraction is ensemble averaging (EA). The conventional EA method usually requires several hundred to thousands of raw SEP input trials to produce an identifiable waveform for latency and amplitude measurement. This is time-consuming and may lead to the failure to detect the dynamic behaviour of the evoked potentials. Therefore, a fast and accurate SEP extraction technique is needed to reduce the measurement time. An adaptive Signal Enhancer (ASE) was applied to extract the weak and noisy SEP signal from anesthetized subjects during surgery. ASE has a self-learning ability in that the weights of the ASE are able to adjust according to each input trial. This ability makes the ASE suitable to track the noisy and time-varying SEP in fewer input trials than EA. The best estimation of the SEP signal can be obtained upon the Hong Kong, for their discussion, comments, advice and support throughout my M.Phil. study. I would also like to express my thanks Prof. Francis H. Y. Chan of the