Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring

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.