Abstract
Brain-computer interfaces (BCI) employ algorithmic procedures of machine learning in order to extract user-specific patterns of high-dimensional EEG features. These patterns are optimised to decode intention-related brain states in real-time. Characteristic BCI applications for paralysed patients are control of active prostheses or speller software. To recognise a user’s motor intention a BCI system utilises individual EEG activation indices, such as the readiness potential or the modulation of regional EEG rhythms. Also beyond the borders of rehabilitation, this neurotechnology enables a growing set of novel application scenarios, e. g., BCIs can serve as optimised feedback tools for the stabilisation of mental states such as vigilance or attention.
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