Conference paper
Modeling Latency and Shape Changes in Trial Based Neuroimaging Data
To overcome poor signal-to-noise ratios in neuroimaging, data sets are often acquired over repeated trials that form a three-way array of spacetimetrials. 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.
Language: | English |
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Publisher: | IEEE |
Year: | 2011 |
Pages: | 439-443 |
Proceedings: | Asilomar Conference on Signals, Systems, and Computers |
ISBN: | 1467303216 , 9781467303217 , 1467303224 , 1467303232 , 9781467303224 and 9781467303231 |
ISSN: | 10586393 and 25762303 |
Types: | Conference paper |
DOI: | 10.1109/ACSSC.2011.6190037 |
ORCIDs: | Mørup, Morten and Hansen, Lars Kai |
Analytical models Brain models Data models Delay EEG data Electroencephalography Visualization bilinear decomposition biomedical MRI blind signal separation blind source separation convolution convolutional representation convolutive multilinear decomposition electroencephalography fMRI data functional magnetic resonance imaging medical signal detection multiple intermixed signal component neurophysiology signal decomposition methods signal detection temporal modeling trial based neuroimaging data trial variability trilinear structure