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Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms AbstractNon-invasive EEG recordings provide for easy and safe access to human neocortical processes which can be exploited for a Brain-Computer Interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. Here, we systematically analyze and furthermore develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: (1) the combination of classifiers each specifically tailored for different physiological phenomena, e.g. slow cortical potential shifts, such as the pre-movement Bereitschaftspotential, or differences in spatio-spectral distributions of brain activity (i.e. focal event-related desynchronizations), and (2) behavioral paradigms inducing the subjects to generate one out of several brain states (multi-class approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show in particular that a suitably arranged interaction between these concepts can significantly boost BCI performances.
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