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it - Information Technology

Methods and Applications of Informatics and Information Technology

Editor-in-Chief: Molitor, Paul

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2196-7032
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Volume 58, Issue 3 (Jun 2016)

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On robust spatial filtering of EEG in nonstationary environments

Wojciech Samek
  • Corresponding author
  • Fraunhofer Heinrich Hertz Institute, Department of Video Coding & Analytics, D-10587 Berlin, Germany
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Published Online: 2016-06-25 | DOI: https://doi.org/10.1515/itit-2016-0023

Abstract

Brain-Computer Interfacing (BCI) is a promising technology for patients that are severely motor-disabled, because it enables them to communicate and interact with the environment. A BCI system decodes user's intentions from brain signals, typically recorded with electroencephalography (EEG), and transmits them to a computer application that, e.g., controls a wheelchair. The efficiency of the system largely depends upon a reliable extraction of informative features from the high-dimensional EEG signal. Spatial filtering is a crucial step in this protocol, however, current approaches are prone to errors when data is contaminated by artifacts or is nonstationary. This article provides an overview of a dissertation, which has addressed the problem of robust spatial filtering in BCI. The contributions of the thesis range from the development of regularization schemes and a robust parameter estimator for spatial filtering, to the formulation of an information geometric view on the spatial filtering problem and the proposal of a new family of algorithms based on robust divergences. The developed methods and concepts are applicable to a variety of problems in machine learning and signal processing.

Keywords: Brain-computer interfacing; robust signal processing; generalized eigenvalue problems; robust estimation; classification; nonstationarity

ACM CCS: Computing methodologies →Machine learning; Mathematics of computing →Probability and statistics; Applied computing →Life and medical sciences

About the article

Wojciech Samek

Dr. Wojciech Samek is head of the Machine Learning group at Fraunhofer Heinrich Hertz Institute and associated researcher at the Berlin Big Data Center. He received the Diploma degree in Computer Science from Humboldt-Universität zu Berlin in 2010 and the Ph.D. degree from the Technische Universität Berlin in 2014. He was scholar of the Studienstiftung des deutschen Volkes and a Ph.D. Fellow at a DFG Research Training Group and the Bernstein Center for Computational Neuroscience Berlin. During his studies he had research stays at the University of Edinburgh, U.K., the NASA Ames Research Center, Mountain View, CA, USA, and ATR International, Kyoto, Japan. His research interests include machine learning, neural networks, signal processing and computer vision.

Fraunhofer Heinrich Hertz Institute, Department of Video Coding & Analytics, D-10587 Berlin, Germany


Accepted: 2016-05-02

Received: 2016-05-02

Published Online: 2016-06-25

Published in Print: 2016-06-28


Citation Information: it - Information Technology, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2016-0023.

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©2016 Walter de Gruyter Berlin/Boston. Copyright Clearance Center

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