27 citations · 48 across the 6 of their papers we have counts for
6 papers
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…
Warped phase coherence: an empirical synchronization measure combining phase and amplitude information
Ludovico Minati, Natsue Yoshimura, Mattia Frasca +2
The entrainment between weakly-coupled nonlinear oscillators, as well as between complex signals such as those representing physiological activity, is frequently assessed in terms…