NDLI: Modeling latency and shape changes in trial based neuroimaging data

Content Provider	IEEE Xplore Digital Library
Author	Morup, M. Hansen, L.K. Madsen, K.H.
Copyright Year	2011
Description	Author affiliation: Section for Cogntive Systems, DTU Informatics, Technical University of Denmark, Denmark (Morup, M.; Hansen, L.K.) \|\| Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Denmark (Madsen, K.H.)
Abstract	To overcome poor signal-to-noise ratios in neuroimaging, data sets are often acquired over repeated trials that form a three-way array of space×time×trials. As neuroimaging data contain multiple inter-mixed signal components blind signal separation and decomposition methods are frequently invoked for exploratory analysis and as a preprocessing step for signal detection. Most previous component analyses have avoided working directly with the tri-linear structure, but resorted to bi-linear models such as ICA, PCA, and NMF. Multi-linear decomposition can exploit consistency over trials and contrary to bi-linear decomposition render unique representations without additional constraints. However, they can degenerate if data does not comply with the given multi-linear structure, e.g., due to time-delays. Here we extend multi-linear decomposition to account for general temporal modeling within a convolutional representation. We demonstrate how this alleviates degeneracy and helps to extract physiologically plausible components. The resulting convolutive multi-linear decomposition can model realistic trial variability as demonstrated in EEG and fMRI data.
Starting Page	439
Ending Page	443
File Size	1007370
Page Count	5
File Format	PDF
ISBN	9781467303217
ISSN	10586393
e-ISBN	9781467303231
DOI	10.1109/ACSSC.2011.6190037
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain models Data models Delay Visualization Electroencephalography Analytical models
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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