Auto-adaptive averaging: detecting artifacts in event-related potential data using a fully automated procedure

The auto-adaptive averaging procedure proposed here classifies artifacts in event-related potential data by optimizing the signal-to-noise ratio. This method rank orders single trials according to the impact of each trial on the ERP average. Then, the minimum residual background noise level in the ERP data is determined at each step in the averaging process. Trials having a negative impact on the residual background noise are discarded from the averaging procedure. Simulations showed that ERP estimates obtained by the auto-adaptive averaging procedure were either better or comparable to those obtained by single trial artifact detection methods at their most optimum configuration, in particular during long duration artifacts. Experimental data from a working memory task further illustrate the effectiveness of the method.