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DC Field | Value | Language |
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dc.contributor.author | Cong, F | - |
dc.contributor.author | Zhou, G | - |
dc.contributor.author | Astikainen, P | - |
dc.contributor.author | Zhao, Q | - |
dc.contributor.author | Wu, Q | - |
dc.contributor.author | Nandi, AK | - |
dc.contributor.author | Hietanen, JK | - |
dc.contributor.author | Ristaniemi, T | - |
dc.contributor.author | Cichocki, A | - |
dc.date.accessioned | 2024-04-02T10:00:01Z | - |
dc.date.available | 2024-04-02T10:00:01Z | - |
dc.date.issued | 2014-11-20 | - |
dc.identifier | ORCiD: Asoke K. Nandi https://orcid.org/0000-0001-6248-2875 | - |
dc.identifier | 1440005 | - |
dc.identifier.citation | Cong, F. et al. (2014) 'Low-rank approximation based non-negative multi-way array decomposition on event-related potentials', International Journal of Neural Systems, 24 (08), 1440005, pp. 1 - 19. doi: 10.1142/S012906571440005X. | en_US |
dc.identifier.issn | 0129-0657 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28668 | - |
dc.description.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. © 2014 World Scientific Publishing Company. | en_US |
dc.description.sponsorship | This work was partially supported by TEKES (Finland) grant 40334/10 ‘Machine Learning for Future Music and Learning Technologies’, and supported by the Fundamental Research Funds for the Central Universities (China, DUT14RC(3)037). A.K. Nandi would like to thank TEKES for their award of the Finland Distinguished Professorship. F. Cong thanks the Research and Innovation Office of the University of Jyväskylä for the international mobility grant (2009, 2010) and thank Dr. Anh Huy Phan (BSIRIKEN, Japan) for helping in learning tensor decomposition. Q. Zhao was partly supported by JSPS Grants-in-Aid for Scientific Research (Grant No.24700154) and the National Natural Science Foundation of China (Grant No. 61202155). F. Cong and G. Zhou contribute equally to this work | en_US |
dc.format.extent | 1 - 19 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | World Scientific Publishing | en_US |
dc.rights | Copyright © The Author(s) 2014. This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License (https://creativecommons.org/licenses/by/4.0/). Further distribution of this work is permitted, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | event-related potential | en_US |
dc.subject | low-rank approximation | en_US |
dc.subject | multi-domain feature | en_US |
dc.subject | non-negative canonical polyadic decomposition | en_US |
dc.subject | non-negative tensor factorization | en_US |
dc.subject | tensor decomposition | en_US |
dc.title | Low-rank approximation based non-negative multi-way array decomposition on event-related potentials | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1142/S012906571440005X | - |
dc.relation.isPartOf | International Journal of Neural Systems | - |
pubs.issue | 8 | - |
pubs.publication-status | Published | - |
pubs.volume | 24 | - |
dc.identifier.eissn | 1793-6462 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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