Visual Evoked Potential Measurement by Adaptive Filtering

The method of ensemble averaging (EA), commonly used to improve signal-to-noise ratio (SNR) in evoked potential measurements, loses potentially useful information regarding response variability across trials. Due to the usually low SNR, it is difficult to extract the response from individual trials. The present paper describes an adaptive signal enhancer (ASE) that is capable of tracking the variation of visual evoked potential (VEP) from trial to trial as well as improving SNR. This ASE requires two inputs: a primary input, which is the raw noise-corrupted response, and a reference input, which is designed for an optimal performance of ASE. We used moving ensemble averaging (MEA) to form the dynamic reference input. Experimental results in humans show a satisfactory performance of the ASE in determining VEP to single stimulus and in tracking the temporal changes of the signal.