bci-challenge-ner-2015 | winning solution to the BCI Challenge

bci-challenge-ner-2015 is a Python library. bci-challenge-ner-2015 has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However bci-challenge-ner-2015 build file is not available. You can download it from GitHub.

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, our Riemannian Geometry framework has been proven very powerful for dealing with this problem (see [2], [3], and the decMeg2014 kaggle challenge). This is partly due to the property of invariance by congruent transformation of the Riemannian metric. Starting from here, 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.