Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
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 [4], 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.