@MASTERSTHESIS\{IMM2005-03623,
    author       = "M. M{\o}rup",
    title        = "Analysis of Brain Data -  Using Multi-Way Array Models on the {EEG}",
    year         = "2005",
    keywords     = "{PARAFAC,} {CANDECOMP,} {ERP,} {EEG,} coherence, {ITPC,} multi-way arrays, tensors, Independent Component Analysis, gamma activity, wavelet analysis, Combined Independence, {HOSVD,} {TUCKER,} Core Consistency Diagnostic",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    note         = "Supervised by Prof. Lars Kai Hansen",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3623-full.html",
    abstract     = "In this thesis the multi-way array model Parallel Factors (PARAFAC) also known as Canonical Decomposition (CANDECOMP) was applied to the event related potential (ERP) of electroencephalographic (EEG) recordings. Previous work done analyzing the {ERP} by {PARAFAC} had encountered great problems of degeneracy in the factors. However, in this thesis it is shown that the problem of degeneracy can be effectively circumvented by imposing non-negativity. Furthermore, the {PARAFAC} analysis was, to my knowledge, for the first time used to analyze the wavelet transformed data of the {ERP}. Through this analysis, it was shown that {PARAFAC} is able to access the correct components of the data. Finally, a novel {PARAFAC} algorithm based on independent component analysis on data having the concept of Combined Independence was proposed. This algorithm proved both fast and efficient in accessing the correct components of simulated as well as real data. In dealing with noise, the algorithm performed even better than the popular {PARAFAC} algorithm based on alternating least squares."
}