dc.contributor.author | Wang, Deqing | |
dc.date.accessioned | 2019-11-25T10:59:22Z | |
dc.date.available | 2019-11-25T10:59:22Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-951-39-7968-3 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/66505 | |
dc.description.abstract | Electroencephalography (EEG) is a powerful technique for the study of human
brain and cognitive neuroscience. Nowadays, more and more EEG data are organized
in high-dimension form, which is called tensor. Tensor decomposition
is just the suitable tool to exploit the multiway data and extract EEG features
that are linked to cognitive processes. Since the high-dimension EEG tensor often
contains a large amount of data points, highly efficient tensor decomposition algorithm
is desired. In addition, EEG tensor are sometimes nonnegative and the
intrinsic features usually have some special properties, such as sparse. In order to
extract meaningful feature components, it is necessary to incorporate constraint
and regularization to tensor decomposition algorithm.
In this dissertation, we study the CANDECOMP/PARAFAC (CP) tensor decomposition
with both nonnegative constraint and sparse regularization, which is
abbreviated as sparse NCP. An inexact block coordinate descent (BCD) framework
is employed for the non-convex sparse NCP problem. Five optimization methods
are employed to solve the sparse NCP, including multiplicative update (MU), alternating
nonnegative least squares/quadratic programming (ANLS/ANQP), hierarchical
altering least squares (HALS), alternating proximal gradient (APG) and
alternating direction method of multipliers (ADMM), all of which are carefully
tailored to the sparse regularization problem. In order to improve the stability, we
also utilize proximal algorithm particularly for ANLS/ANQP and HALS.
Applications on real-world EEG datasets are carried out. First, we use NCP
to decompose a fifth-order event-related potential (ERP) tensor, which was collected
by proprioceptive stimuli on human hands. Next, ongoing EEG tensors are
analyzed using sparse NCP. The data were collected by naturalistic and continuous
music stimulus. Finally, we analyze two modalities of ongoing EEG tensor
and music signals simultaneously by N-way partial least square (N-PLS).
In conclusion, our designed tensor decomposition methods with constraint
and regularization are able to decompose high-order tensor data efficiently and
extract meaningful EEG features linked to cognitive processes.
Keywords: Tensor decomposition, nonnegative CANDECOMP/PARAFAC,
sparse regularization, block coordinate descent, EEG data analysis | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Jyväskylän yliopisto | |
dc.relation.ispartofseries | JYU dissertations | |
dc.relation.haspart | <b>Artikkeli I:</b> Deqing Wang, Xiulin Wang, Tapani Ristaniemi and Fengyu Cong. Sparse Nonnegative Tensor Decomposition in Inexact Block Coordinate Descent Framework. <i>Submitted to a journal.</i> | |
dc.relation.haspart | <b>Artikkeli II:</b> Wang, D., Cong, F., & Ristaniemi, T. (2019). Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm. In <i>ICASSP 2019 : Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 3457-3461). IEEE.</i> <a href="https://doi.org/10.1109/ICASSP.2019.8683217"target="_blank"> DOI: 10.1109/ICASSP.2019.8683217</a> | |
dc.relation.haspart | <b>Artikkeli III:</b> Wang, D., Zhu, Y., Ristaniemi, T., & Cong, F. (2018). Extracting multi-mode ERP features using fifth-order nonnegative tensor decomposition. <i>Journal of Neuroscience Methods, 308, 240-247.</i> <a href="https://doi.org/10.1016/j.jneumeth.2018.07.020"target="_blank"> DOI: 10.1016/j.jneumeth.2018.07.020</a> | |
dc.relation.haspart | <b>Artikkeli IV:</b> Wang, D., Wang, X., Zhu, Y., Toiviainen, P., Huotilainen, M., Ristaniemi, T., & Cong, F. (2018). Increasing Stability of EEG Components Extraction Using Sparsity Regularized Tensor Decomposition. In <i>T. Huang, J. Lv, C. Sun, & A. V. Tuzikov (Eds.), ISNN 2018 : Advances in Neural Networks : 15th International Symposium on Neural Networks, Proceedings (pp. 789-799). Springer International Publishing.</i> <a href="https://doi.org/10.1007/978-3-319-92537-0_89"target="_blank"> DOI: 10.1007/978-3-319-92537-0_89</a> | |
dc.relation.haspart | <b>Artikkeli V:</b> Wang, D., Cong, F., Zhao, Q., Toiviainen, P., Nandi, A. K., Huotilainen, M., . . . , & Cichocki, A. (2016). Exploiting ongoing EEG with multilinear partial least squares during free-listening to music. In <i>F. A. N. Palmieri, A. Uncini, K. Diamantaras, & J. Larsen (Eds.), Proceedings of MLSP 2016 : IEEE International Workshop on Machine Learning for Signal Processing. Institute of Electrical and Electronic Engineers.</i> <a href="https://doi.org/10.1109/MLSP.2016.7738849"target="_blank"> DOI: 10.1109/MLSP.2016.7738849</a> | |
dc.rights | In Copyright | |
dc.subject | EEG | |
dc.subject | signaalinkäsittely | |
dc.subject | signaalianalyysi | |
dc.subject | algoritmit | |
dc.subject | kognitiivinen neurotiede | |
dc.subject | aivotutkimus | |
dc.subject | tensor decomposition | |
dc.subject | nonnegative CANDECOMP/PARAFAC | |
dc.subject | sparse regularization | |
dc.subject | block coordinate descent | |
dc.subject | EEG data analysis | |
dc.title | Extracting Meaningful EEG Features Using Constrained Tensor Decomposition | |
dc.type | Diss. | |
dc.identifier.urn | URN:ISBN:978-951-39-7968-3 | |
dc.contributor.yliopisto | University of Jyväskylä | en |
dc.contributor.yliopisto | Jyväskylän yliopisto | fi |
dc.relation.issn | 2489-9003 | |
dc.rights.copyright | © The Author & University of Jyväskylä | |
dc.rights.accesslevel | openAccess | |
dc.type.publication | doctoralThesis | |
dc.format.content | fulltext | |
dc.rights.url | https://rightsstatements.org/page/InC/1.0/ | |