Canonical polyadic and block term decompositions to fuse EEG, phenotypic scores, and structural MRI of children with early-onset epilepsy

Dron, Noramon; Chin, Richard; Escudero, Javier

doi:10.23919/eusipco47968.2020.9287765

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…Future work will include alternative decomposition models (e.g., BTD (Dron, Chin, & Escudero, 2021)) in the fusion model and comparisons with additional matrix-based methods, notably those based on independent vector analysis (Adalı et al, 2015a). Ways of semiautomatically selecting the values of the λ parameters should also be sought for.…”

Section: Discussionmentioning

confidence: 99%

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis

Chatzichristos

Kofidis

Paesschen

et al. 2021

Human Brain Mapping

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Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG-fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.

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Section: Discussionmentioning

confidence: 99%

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis

Chatzichristos

Kofidis

Paesschen

et al. 2021

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…In this context, multimodal fusion will gain importance. We believe that tensor techniques will play an important role here as well, as they allow to handle heterogeneous data (e.g., EEG, structural images, and phenotypic scores [157]) simultaneously and-again-to impose constraints that ensure interpretability.…”

Section: Future Challengesmentioning

confidence: 99%