The goal of this challenge was to detect error related potential recorded during a p300 spelling task. The classification must be done across subjects, i.e. training and test sets were composed by different subjects. This is a hard task due to the high inter-subjects variability of EEG. However, my Riemannian Geometry framework has been proven very powerful for dealing with this problem.

I entered this challenge with Rafal Cycon and C├ędric Gouy-pailler.

The main difficulties of this challenge were to deal with the relatively high number of electrodes and to avoid overfitting. We overcome the first issue by using a channel selection algorithm, and the second by using a bagging procedure and an appropriate cross-validation methodology.