Modeling Latency and Shape Changes in Trial Based Neuroimaging Data

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2011

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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.
Original languageEnglish
Title of host publication2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
PublisherIEEE
Publication date2011
Pages439-443
ISBN (print)978-1-4673-0321-7
DOIs
StatePublished

Conference

ConferenceAsilomar Conference on Signals, Systems, and Computers
CountryUnited States
CityPacific Grove, CA
Period06/11/1109/11/11

Bibliographical note

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