TY - JOUR
T1 - Machine learning methods of the Berlin brain-computer interface
AU - Vidaurre, Carmen
AU - Sannelli, Claudia
AU - Samek, Wojciech
AU - Dähne, Sven
AU - Müller, Klaus Robert
N1 - Funding Information:
The auffihors acknowledge funding ffly ffihe German Research Foundaffiion (DFG) granffi nos. MU 987/19-1, MU 987/14-1, MU 987/3-2, and supporffi ffly ffihe Bernsffiein Cenffier for Compuffiaffiional ∆euroscience Berlin ffihrough ffihe graduaffie program GRK 1589/1
Publisher Copyright:
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - This paper is a compilation of the most recent machine learning methods used in the Berlin Brain-Computer Interface. In the field of Brain-Computer Interfacing, machine learning has been mainly used to extract meaningful features from noisy signals of large dimensionality and to classify them to transform them into computer commands. Recently, our group developed different methods to deal with noisy, non-stationary and high dimensional signals. These approaches can be seen as variants of the algorithm Common Spatial Patterns (CSP). All of them outperform CSP in the different conditions for which they were developed.
AB - This paper is a compilation of the most recent machine learning methods used in the Berlin Brain-Computer Interface. In the field of Brain-Computer Interfacing, machine learning has been mainly used to extract meaningful features from noisy signals of large dimensionality and to classify them to transform them into computer commands. Recently, our group developed different methods to deal with noisy, non-stationary and high dimensional signals. These approaches can be seen as variants of the algorithm Common Spatial Patterns (CSP). All of them outperform CSP in the different conditions for which they were developed.
KW - Adaptive systems
KW - Brain-computer interfacing
KW - Electroencephalogram
KW - Motor imagery
KW - Multimodal analysis
KW - Non-stationary analysis
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U2 - 10.1016/j.ifacol.2015.10.181
DO - 10.1016/j.ifacol.2015.10.181
M3 - Conference article
AN - SCOPUS:84992478782
VL - 28
SP - 447
EP - 452
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 20
T2 - 9th IFAC Symposium on Biological and Medical Systems, BMS 2015
Y2 - 31 August 2015 through 2 September 2015
ER -