There are a number of limitations to existing usability testing methods, including surveys, interviews, talk-alouds, and participant observations. These limitations include subject bias, poor recall, and inability to capture fleeting events, such as when a UI functions or behaves in a manner that contradicts user expectations. One possible solution to these problems is to use electrophysiological indicators to monitor user interaction with the UI. We propose using event related potentials (ERP), and the error potential (ErrP) more specifically, to capture moment-to-moment interactions that lead to violations in user expectations. An ERP is a response generated in the brain to stimuli, while the ErrP is a more specific signal shown to be elicited by subject error. In this experiment we monitored subjects using a 10-channel electroencephalogram (EEG) as they completed a range of simple web browsing tasks. However, roughly 1/3 of the time subjects were confronted with poor UI design features (e.g., broken links). We then used statistical and machine learning techniques to classify the data and found that we were able to accurately identify the presence of error potentials. Furthermore, the ErrP was present when the subjects encountered a UI design flaw, but only during the more 'overt' examples of our design flaws. Results support our hypothesis that ERPs and ErrPs, can be used to identify UI design flaws for a variety of systems, from web sites to video games.