ERP Detection Based on Smoothness Priors

Event-related potentials (ERPs) are weak activities in electroencephalography (EEG) in response to different stimuli. Detecting these potentials is of great value for many diagnostic and practical purposes. However, this is rather hard because these signals have a low signal-to-noise ratio. In this work, we present a novel approach toward ERP detection based on the fact that this template has a smooth structure compared to background EEG. A generalized likelihood ratio test is adopted to realize our algorithm. The proposed method is investigated for ERP detection from real datasets, and it demonstrates to be very effective compared to conventional state-of-the-art methods.