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 language | English |
|---|---|
| Title of host publication | 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781479974948 |
| DOIs | |
| Publication status | Published - 2015 Mar 30 |
| Externally published | Yes |
| Event | 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of Duration: 2015 Jan 12 → 2015 Jan 14 |
Other
| Other | 2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon-Do |
| Period | 15/1/12 → 15/1/14 |
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ASJC Scopus subject areas
- Human-Computer Interaction
- Cognitive Neuroscience
- Sensory Systems
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Machine learning and BCI
AU - Muller, Klaus
PY - 2015/3/30
Y1 - 2015/3/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84927921754&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84927921754&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2015.7073023
DO - 10.1109/IWW-BCI.2015.7073023
M3 - Conference contribution
AN - SCOPUS:84927921754
SN - 9781479974948
BT - 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -