Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 167, Issue 1, Pages 82-90Publisher
ELSEVIER
DOI: 10.1016/j.jneumeth.2007.09.022
Keywords
EEG; sensorimotor rhythms; alpha-rhythm; single-trial EEG-analysis; real-time; machine learning; mental state monitoring
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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. (C) 2007 Elsevier B.V. All rights reserved.
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