9 citations · 58 across the 23 of their papers we have counts for
11 papers · 1 filter
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
Wenwei Luo, Wanguang Yin, Quanying Liu +1
The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusin…
Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding
Wanguang Yin, Zhichao Liang, Jianguo Zhang +1
Partial least square regression (PLSR) is a widely-used statistical model to reveal the linear relationships of latent factors that comes from the independent variables and depende…
Embedding Decomposition for Artifacts Removal in EEG Signals
Junjie Yu, Chenyi Li, Kexin Lou +2
Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of…
Deep Auto-encoder with Neural Response
Xuming Ran, Jie Zhang, Ziyuan Ye +4
Artificial neural network (ANN) is a versatile tool to study the neural representation in the ventral visual stream, and the knowledge in neuroscience in return inspires ANN models…
Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization
Chen Wei, Kexin Lou, Zhengyang Wang +3
EEG source localization is an important technical issue in EEG analysis. Despite many numerical methods existed for EEG source localization, they all rely on strong priors and the…
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review
Jie Yuan, Xuming Ran, Keyin Liu +4
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some review…
Riemannian Manifold Optimization for Discriminant Subspace Learning
Wanguang Yin, Zhengming Ma, Quanying Liu
Linear discriminant analysis (LDA) is a widely used algorithm in machine learning to extract a low-dimensional representation of high-dimensional data, it features to find the orth…
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction
Wanguang Yin, Youzhi Qu, Zhengming Ma +1
Tensor decomposition is an effective tool for learning multi-way structures and heterogeneous features from high-dimensional data, such as the multi-view images and multichannel el…
Bigeminal Priors Variational auto-encoder
Xuming Ran, Mingkun Xu, Qi Xu +2
Variational auto-encoders (VAEs) are an influential and generally-used class of likelihood-based generative models in unsupervised learning. The likelihood-based generative models…
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
Xuming Ran, Mingkun Xu, Lingrui Mei +2
Variational autoencoders (VAEs) are influential generative models with rich representation capabilities from the deep neural network architecture and Bayesian method. However, VAE…
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
Quanying Liu, Haiyan Wu, Anqi Liu
We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. T…