L
Li-Dan Kuang
Researcher at Changsha University of Science and Technology
Publications - 13
Citations - 350
Li-Dan Kuang is an academic researcher from Changsha University of Science and Technology. The author has contributed to research in topics: Tensor & Matrix decomposition. The author has an hindex of 4, co-authored 13 publications receiving 269 citations. Previous affiliations of Li-Dan Kuang include Dalian University of Technology.
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Journal ArticleDOI
Tensor decomposition of EEG signals: A brief review
Fengyu Cong,Fengyu Cong,Qiu-Hua Lin,Li-Dan Kuang,Xiao-Feng Gong,Piia Astikainen,Tapani Ristaniemi +6 more
TL;DR: This review summarizes the current progress of tensor decomposition of EEG signals with three aspects, and two fundamental tensor decompposition models, canonical polyadic decomposition (CPD) and Tucker decomposition, are introduced and compared.
Journal ArticleDOI
Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint
TL;DR: Improvements of the proposed method over three complex-valued algorithms, namely, tensor-based spatial ICA, shift-invariant CPD and CPD without spatiotemporal constraints are demonstrated.
Proceedings ArticleDOI
Sample Augmentation for Classification of Schizophrenia Patients and Healthy Controls Using ICA of fMRI Data and Convolutional Neural Networks
TL;DR: This work proposes three strategies for both prior to and post ICA sample augmentation in the ICA-CNN framework to increase the number of samples by performing spatial smoothing and band-pass filtering on the observed fMRI data before ICA, and spatial smoothed on the spatial maps after ICA.
Proceedings ArticleDOI
An adaptive fixed-point IVA algorithm applied to multi-subject complex-valued FMRI data
TL;DR: An adaptive fixed-point IVA algorithm is proposed by taking into account the extremely noisy nature, large variability of the source component vector (SCV) distribution, and non-circularity of the complex-valued fMRI data.
Proceedings ArticleDOI
Post-ICA phase de-noising for resting-state complex-valued FMRI data
TL;DR: This work presents an efficient method for de-noising SM estimates which makes full use of complex-valued resting-state fMRI data and introduces a new phase range detection strategy for a specific SM component based on correlation with its reference.