Machine learning and BCI

Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A main motivation for multimodal imaging has been the possibility to enhance medical diagnosis[1]. Beyond this original medical motivation the fusion of multiple modalities has created successful interesting research opportunities that have furthered our understanding of the brain and cognition[15]. In BCI recently multimodal fusion concepts have received great attention under the label hybrid BCI[13]. Fusing information has also been a very common practice in the sciences and engineering [17]. Recently a family of novel multimodal data analysis methods have emerged that can extract nonlinear relations between data[1,2,5-10]. They are rooted in the modern machine learning and signal processing techniques that are now available for analysing EEG, for decoding mental states etc[3,11,12,14,16]. The talk will first discuss recent multimodal analysis techniques such as SPoC[5-7]. Furthermore if time permits we will discuss a novel reliable method for estimating the Hurst exponent, a quantity that has recently become popular for describing network properties and is being used for diagnostic purposes[4]. Both nonlinear techniques allow for a better and more reliable and robust analysis of complex phenomena in neurophysiological data.

Original languageEnglish
Title of host publication3rd International Winter Conference on Brain-Computer Interface, BCI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479974948
DOIs
Publication statusPublished - 2015 Mar 30
Externally publishedYes
Event2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of
Duration: 2015 Jan 122015 Jan 14

Other

Other2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
CountryKorea, Republic of
CityGangwon-Do
Period15/1/1215/1/14

Fingerprint

Learning systems
Motivation
Fusion reactions
Multimodal Imaging
Electroencephalography
Cognition
Decoding
Labels
Brain
Signal processing
Imaging techniques
Research
Machine Learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Sensory Systems

Cite this

Muller, K. (2015). Machine learning and BCI. In 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 [7073023] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2015.7073023

Machine learning and BCI. / Muller, Klaus.

3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7073023.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Muller, K 2015, Machine learning and BCI. in 3rd International Winter Conference on Brain-Computer Interface, BCI 2015., 7073023, Institute of Electrical and Electronics Engineers Inc., 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015, Gangwon-Do, Korea, Republic of, 15/1/12. https://doi.org/10.1109/IWW-BCI.2015.7073023
Muller K. Machine learning and BCI. In 3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7073023 https://doi.org/10.1109/IWW-BCI.2015.7073023
Muller, Klaus. / Machine learning and BCI. 3rd International Winter Conference on Brain-Computer Interface, BCI 2015. Institute of Electrical and Electronics Engineers Inc., 2015.
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