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Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition

Yaqub Jonmohamadi, Suresh Muthukumaraswamy, Joseph Chen, Jonathan Roberts, Ross Crawford, Ajay Pandey
doi: https://doi.org/10.1101/685941
Yaqub Jonmohamadi
1Department of Electrical Engineering and Computer Science, Queensland University of Technology, Australia
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  • For correspondence: y.jonmo@qut.edu.au
Suresh Muthukumaraswamy
2School of Pharmacy, The University of Auckland, New Zealand
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Joseph Chen
2School of Pharmacy, The University of Auckland, New Zealand
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Jonathan Roberts
1Department of Electrical Engineering and Computer Science, Queensland University of Technology, Australia
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Ross Crawford
1Department of Electrical Engineering and Computer Science, Queensland University of Technology, Australia
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Ajay Pandey
1Department of Electrical Engineering and Computer Science, Queensland University of Technology, Australia
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Abstract

The fusion of simultaneously recorded EEG and fMRI data is of great value to neuroscience research due to the complementary properties of the individual modalities. Traditionally, techniques such as PCA and ICA, which rely on strong strong non-physiological assumptions such as orthogonality and statistical independence, have been used for this purpose. Recently, tensor decomposition techniques such as parallel factor analysis have gained more popularity in neuroimaging applications as they are able to inherently contain the multidimensionality of neuroimaging data and achieve uniqueness in decomposition without imposing strong assumptions. Previously, the coupled matrix-tensor decomposition (CMTD) has been applied for the fusion of the EEG and fMRI. Only recently the coupled tensor-tensor decomposition (CTTD) has been proposed. Here for the first time, we propose the use of CTTD of a 4th order EEG tensor (space, time, frequency, and participant) and 3rd order fMRI tensor (space, time, participant), coupled partially in time and participant domains, for the extraction of the task related features in both modalities. We used both the sensor-level and source-level EEG for the coupling. The phase shifted paradigm signals were incorporated as the temporal initializers of the CTTD to extract the task related features. The validation of the approach is demonstrated on simultaneous EEG-fMRI recordings from six participants performing an N-Back memory task. The EEG and fMRI tensors were coupled in 9 components out of which 7 components had a high correlation (more than 0.85) with the task. The result of the fusion recapitulates the well-known attention network as being positively, and the default mode network working negatively time-locked to the memory task.

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Posted July 02, 2019.
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Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition
Yaqub Jonmohamadi, Suresh Muthukumaraswamy, Joseph Chen, Jonathan Roberts, Ross Crawford, Ajay Pandey
bioRxiv 685941; doi: https://doi.org/10.1101/685941
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Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition
Yaqub Jonmohamadi, Suresh Muthukumaraswamy, Joseph Chen, Jonathan Roberts, Ross Crawford, Ajay Pandey
bioRxiv 685941; doi: https://doi.org/10.1101/685941

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