title: Low-rank approximation based non-negative multi-way array decomposition on event-related potentials.
abstract: Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.
authors.affiliation: Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, China , Department of Mathematical Information Technology, University of Jyväskylä, Finland.
authors.forename: Fengyu
authors.initials: F
authors.lastname: Cong
authors.forename: Guoxu
authors.initials: G
authors.lastname: Zhou
authors.forename: Piia
authors.initials: P
authors.lastname: Astikainen
authors.forename: Qibin
authors.initials: Q
authors.lastname: Zhao
authors.forename: Qiang
authors.initials: Q
authors.lastname: Wu
authors.forename: Asoke K
authors.initials: AK
authors.lastname: Nandi
authors.forename: Jari K
authors.initials: JK
authors.lastname: Hietanen
authors.forename: Tapani
authors.initials: T
authors.lastname: Ristaniemi
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
mesh_terms: D000328:Adult; D003627:Data Interpretation, Statistical; D003863:Depression / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods*; D004644:Emotions / Q000502:physiology; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged
keywords: Event-related potential; low-rank approximation; multi-domain feature; non-negative canonical polyadic decomposition; non-negative tensor factorization; tensor decomposition