Machine learning methods of the Berlin brain-computer interface

Carmen Vidaurre; Claudia Sannelli; Wojciech Samek; Sven Dähne; Klaus Robert Müller

doi:10.1016/j.ifacol.2015.10.181

Machine learning methods of the Berlin brain-computer interface

Carmen Vidaurre, Claudia Sannelli, Wojciech Samek, Sven Dähne, Klaus Robert Müller

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	447-452
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	28
Issue number	20
DOIs	https://doi.org/10.1016/j.ifacol.2015.10.181
Publication status	Published - 2015 Sep 1
Event	9th IFAC Symposium on Biological and Medical Systems, BMS 2015 - Berlin, Germany Duration: 2015 Aug 31 → 2015 Sep 2

Keywords

Adaptive systems
Brain-computer interfacing
Electroencephalogram
Motor imagery
Multimodal analysis
Non-stationary analysis

ASJC Scopus subject areas

Control and Systems Engineering

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abstract = "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.",

keywords = "Adaptive systems, Brain-computer interfacing, Electroencephalogram, Motor imagery, Multimodal analysis, Non-stationary analysis",

author = "Carmen Vidaurre and Claudia Sannelli and Wojciech Samek and Sven D{\"a}hne and M{\"u}ller, {Klaus Robert}",

note = "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: {\textcopyright} 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 9th IFAC Symposium on Biological and Medical Systems, BMS 2015 ; Conference date: 31-08-2015 Through 02-09-2015",

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doi = "10.1016/j.ifacol.2015.10.181",

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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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VL - 28

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JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

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T2 - 9th IFAC Symposium on Biological and Medical Systems, BMS 2015

Y2 - 31 August 2015 through 2 September 2015

ER -

Machine learning methods of the Berlin brain-computer interface

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Keywords

ASJC Scopus subject areas

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