Peter Meinicke, Matthias Kaper, Florian Hoppe, Manfred Heumann, Helge Ritter
In this paper we present results of a study on brain computer interfacing. We adopted an approach of Farwell & Donchin , which we tried to improve in several aspects. The main objective was to improve the trans- fer rates based on ofﬂine analysis of EEG-data but within a more realistic setup closer to an online realization than in the original studies. The ob- jective was achieved along two different tracks: on the one hand we used state-of-the-art machine learning techniques for signal classiﬁcation and on the other hand we augmented the data space by using more electrodes for the interface. For the classiﬁcation task we utilized SVMs and, as mo- tivated by recent ﬁndings on the learning of discriminative densities, we accumulated the values of the classiﬁcation function in order to combine several classiﬁcations, which ﬁnally lead to signiﬁcantly improved rates as compared with techniques applied in the original work. In combina- tion with the data space augmentation, we achieved competitive transfer rates at an average of 50.5 bits/min and with a maximum of 84.7 bits/min.