9 citations · 58 across the 23 of their papers we have counts for
23 papers
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Peizhen Yang, Xinke Shen, Zongsheng Li +3
Identifying causal relationships among distinct brain areas, known as effective connectivity, holds key insights into the brain's information processing and cognitive functions. El…
Mapping effective connectivity by virtually perturbing a surrogate brain
Zixiang Luo, Kaining Peng, Zhichao Liang +6
Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approache…
Network analysis on cortical morphometry in first-episode schizophrenia
Mowen Yin, Weikai Huang, Zhichao Liang +2
First-episode schizophrenia (FES) results in abnormality of brain connectivity at different levels. Despite some successful findings on functional and structural connectivity of FE…
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…
Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network
Ziyuan Ye, Youzhi Qu, Zhichao Liang +2
Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning method…
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…
Transfer learning to decode brain states reflecting the relationship between cognitive tasks
Youzhi Qu, Xinyao Jian, Wenxin Che +3
Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks,…
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…
Kuramoto model based analysis reveals oxytocin effects on brain network dynamics
Shuhan Zheng, Zhichao Liang, Youzhi Qu +3
The oxytocin effects on large-scale brain networks such as Default Mode Network (DMN) and Frontoparietal Network (FPN) have been largely studied using fMRI data. However, these stu…
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
Zhichao Liang, Zixiang Luo, Keyin Liu +2
Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actua…
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…
A novel convolutional neural network model to remove muscle artifacts from EEG
Haoming Zhang, Chen Wei, Mingqi Zhao +2
The recorded electroencephalography (EEG) signals are usually contaminated by many artifacts. In recent years, deep learning models have been used for denoising of electroencephalo…
EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising
Haoming Zhang, Mingqi Zhao, Chen Wei +3
Deep learning networks are increasingly attracting attention in various fields, including electroencephalography (EEG) signal processing. These models provided comparable performan…
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…
Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control
Yorie Nakahira, Quanying Liu, Terrence J. Sejnowski +1
Nervous systems sense, communicate, compute and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise and saturation. Nevertheles…
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…
Fitts' Law for speed-accuracy trade-off describes a diversity-enabled sweet spot in sensorimotor control
Yorie Nakahira, Quanying Liu, Terrence J. Sejnowski +1
Human sensorimotor control exhibits remarkable speed and accuracy, and the tradeoff between them is encapsulated in Fitts' Law for reaching and pointing. While Fitts related this t…
WheelCon: A wheel control-based gaming platform for studying human sensorimotor control
Quanying Liu, Yorie Nakahira, Ahkeel Mohideen +5
Feedback control theory has been extensively implemented to theoretically model human sensorimotor control. However, experimental platforms capable of manipulating important compon…