@CONFERENCE\{IMM2011-06201,
    author       = "M. M{\o}rup and L. K. Hansen and K. H. Madsen",
    title        = "Modeling Latency and Shape Changes in Trial Based Neuroimaging Data",
    year         = "2011",
    booktitle    = "invited paper, Asilomar-SSC",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6201-full.html",
    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 x time x 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."
}