27 citations · 48 across the 8 of their papers we have counts for
8 papers
Robust Sparse Bayesian Learning Based on Minimum Error Entropy for Noisy High-Dimensional Brain Activity Decoding
Yuanhao Li, Badong Chen, Wenjun Bai +2
Objective: Sparse Bayesian learning provides an effective framework to solve high-dimensional problems in brain signal decoding. However, conventional likelihoods regarding data di…
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging
Yuanhao Li, Badong Chen, Zhongxu Hu +4
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian…
Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Yuanhao Li, Badong Chen, Natsue Yoshimura +2
Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity. However, existing sparse Bayesian…
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination
Yuanhao Li, Badong Chen, Okito Yamashita +2
Sparseness and robustness are two important properties for many machine learning scenarios. In the present study, regarding the maximum correntropy criterion (MCC) based robust reg…
Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding
Yuanhao Li, Badong Chen, Yuxi Shi +2
Recent studies have utilized sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's intentions and mental states, se…
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
Yuanhao Li, Badong Chen, Gang Wang +2
The Partial Least Square Regression (PLSR) exhibits admirable competence for predicting continuous variables from inter-correlated brain recordings in the brain-computer interface.…
Restricted Minimum Error Entropy Criterion for Robust Classification
Yuanhao Li, Badong Chen, Natsue Yoshimura +1
The minimum error entropy (MEE) criterion has been verified as a powerful approach for non-Gaussian signal processing and robust machine learning. However, the implementation of ME…
Bias-Compensated Normalized Maximum Correntropy Criterion Algorithm for System Identification with Noisy Input
Wentao Ma, Dongqiao Zheng, Yuanhao Li +2
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with n…