Modeling Latency and Shape Changes in Trial Based Neuroimaging Data |
| | 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. | | Type | Conference paper [With referee] | | Conference | invited paper, Asilomar-SSC | | Year | 2011 | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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