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Indexed by:期刊论文

Date of Publication:2014-12-01

Journal:INTERNATIONAL JOURNAL OF NEURAL SYSTEMS

Included Journals:SCIE、EI、PubMed

Volume:24

Issue:8

Page Number:CP2-U20

ISSN No.:0129-0657

Key Words:Event-related potential; low-rank approximation; multi-domain feature; non-negative canonical polyadic decomposition; non-negative tensor factorization; tensor decomposition

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.