Visual Evoked Potential enhancement by an Artificial Neural Network Filter

The application of an artificial neural network filter (ANNF) to estimate the visual evoked potential (VEP) is presented. VEP is the gross electrical response of the brain to visual stimuli. Due to the low SNR, it is difficult to extract response from individual stimulus trials. The ANNF we used estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even when the noise is colored. The ANNF is trained through back-error propagation with a data set consisting of a training signal and a target signal. The training signal is the raw VEP from a single trial having a SNR of about −5 dB, while the target signal has a higher SNR which is achieved by ensemble averaging 100 stimulus trials. Simulated signals were generated to test the performance of the ANNF. Results show that the ANNF could greatly enhance the SNR of the VEP to single visual stimulus. Thus the total number of ensembles is reduced. In clinical applications, the traditional ensemble averaging method requires a hundred ensembles to determine the VEP. When ANNF is used, about 20 ensembles are sufficient for the same purpose.