Modeling Latency and Shape Changes in Trial Based Neuroimaging Data

Morten Mørup, Lars Kai Hansen, Kristoffer Hougaard Madsen

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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)
    Publication date2011
    ISBN (Print)978-1-4673-0321-7
    Publication statusPublished - 2011
    EventAsilomar Conference on Signals, Systems, and Computers - Pacific Grove, CA, United States
    Duration: 6 Nov 20119 Nov 2011


    ConferenceAsilomar Conference on Signals, Systems, and Computers
    CountryUnited States
    CityPacific Grove, CA

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