TY - JOUR
T1 - Machine learning for real-time single-trial EEG-analysis
T2 - From brain-computer interfacing to mental state monitoring
AU - Müller, Klaus Robert
AU - Tangermann, Michael
AU - Dornhege, Guido
AU - Krauledat, Matthias
AU - Curio, Gabriel
AU - Blankertz, Benjamin
N1 - Funding Information:
We gratefully acknowledge financial support by the Bundesministerium für Bildung und Forschung (BMBF), 01IBE01A/B and 01IGQ0414, by the Deutsche Forschungsgemeinschaft (DFG), FOR 375/B1, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008/1/15
Y1 - 2008/1/15
N2 - 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.
AB - 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.
KW - EEG
KW - Machine learning
KW - Mental state monitoring
KW - Real-time
KW - Sensorimotor rhythms
KW - Single-trial EEG-analysis
KW - α-Rhythm
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U2 - 10.1016/j.jneumeth.2007.09.022
DO - 10.1016/j.jneumeth.2007.09.022
M3 - Article
C2 - 18031824
AN - SCOPUS:36549029758
SN - 0165-0270
VL - 167
SP - 82
EP - 90
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 1
ER -