Graph regularised tensor factorisation of EEG signals based on network connectivity measures

Loukianos Spyrou, Javier Escudero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Tensor factorisation is a decomposition method for high dimensional data and is primarily used as an extension of singular value decomposition. It is used to estimate the prominent factors in some signal and recently has been employed in the biomedical fields. Regularised tensor factorisation attempts to alleviate overfitting and small sample size estimation errors by constraining the obtained solution to satisfy some metric. In this work, we provide a novel extension to the theory of graph regularisation for regularising multiple graphs and we employ graph regularised tensor factorisation on an electroencephalogram (EEG) dataset. We utilise brain connectivity networks as the basis of our graphs. Subsequently, we perform graph regularised tensor factorisation on the EEG data in order to reduce the noise and interference inherent to the EEG. We demonstrate the efficacy of the algorithm theoretically and on some real EEG examples.
Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages944-948
Number of pages5
DOIs
Publication statusPublished - 8 Mar 2017
EventThe 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2017) - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Conference

ConferenceThe 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2017)
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

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