3 citations · 6 across the 3 of their papers we have counts for
10 papers
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…
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…
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…
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…