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Data driven neuroergonomic optimization of BCI stimuli

Neuroergonomic design of Brain-Computer Interface (BCI) experiments can be realized as a data driven optimization of stimuli. The goal of this process is to increase the number and information content of class-discriminant features of the EEG for the BCI task at hand. While existing electrophysiological literature indicated the inuence of confounding variables on e.g. P300 latency and amplitude by group studies and grand average statistics, the BCI performance can be boosted by a large amount when optimized stimuli are explored and designed for individual users and then used in single-trials. The potential of this design principle is shown in an oine analysis for the example of a visual ( n = 8) and an auditory (n = 5) ERP study with healthy subjects, where the optimization of stimulus parameters leads to both a decrease in classication errors and an increase in speed.