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 |
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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 |
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Country/Territory | Korea, Republic of |
City | Gangwon-Do |
Period | 15/1/12 → 15/1/14 |
ASJC Scopus subject areas
- Human-Computer Interaction
- Cognitive Neuroscience
- Sensory Systems