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.
Müller
Klaus-Robert
KR
Technical University Berlin, Str. des 17. Juni 135, 10623 Berlin, Germany. klaus@first.fhg.de
Tangermann
Michael
M
Dornhege
Guido
G
Krauledat
Matthias
M
Curio
Gabriel
G
Blankertz
Benjamin
B
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography; D004576:Electromyography; D005246:Feedback; D007839:Functional Laterality; D006801:Humans; D008328:Man-Machine Systems; D008606:Mental Processes / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013057:Spectrum Analysis; D014584:User-Computer Interface