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, 167 (1), 82-90

Machine Learning for Real-Time Single-Trial EEG-analysis: From Brain-Computer Interfacing to Mental State Monitoring

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Machine Learning for Real-Time Single-Trial EEG-analysis: From Brain-Computer Interfacing to Mental State Monitoring

Klaus-Robert Müller et al. J Neurosci Methods.

Abstract

Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain-computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.

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