DETECTION OF BRAIN-STEM AUDITORY-EVOKED POTENTIAL BY ADAPTIVE FILTERING

A method of detecting brainstem auditory evoked potential (BAEP) using adaptive signal enhancement (ASE) is proposed and tested in humans an d cats. The ASE in this system estimates the signal component of the p rimary input, which is correlated with the reference input to the adap tive filter. The reference input is carefully designed to make an opti mal and rapid estimation of the signal corrupted with noise, such as o ngoing EEG. With a good choice of reference input, it is possible to t rack the variability of BAEP efficiently and rapidly. Moreover, the nu mber of repetitions required could be markedly reduced and the result of the system is superior to that of ensemble averaging (EA). To detec t BAEP in cats, only 30 ensemble averages are needed to obtain a reaso nable reference input to the adaptive filter, and, for humans, 350-750 ensemble averages are sufficient for a satisfactory result. Using the LMS adaptive algorithm, individual BAEP can be obtained in real-time.