# Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals

arXiv: https://arxiv.org/abs/2106.13086 · HTML: https://mlsanity.com/paper/2106.13086/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 57 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima](https://mlsanity.com/paper/1305.2436/) (2013)
- [Restricted Minimum Error Entropy Criterion for Robust Classification](https://mlsanity.com/paper/1909.02707/) (2019)

## Built on by (most cited first, 3 of 3)

- [Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings](https://mlsanity.com/paper/2404.15309/) (2024)
- [Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging](https://mlsanity.com/paper/2408.14843/) (2024)
- [Robust Sparse Bayesian Learning Based on Minimum Error Entropy for Noisy High-Dimensional Brain Activity Decoding](https://mlsanity.com/paper/2508.11657/) (2025)

## Abstract (arXiv)

The Partial Least Square Regression (PLSR) exhibits admirable competence for predicting continuous variables from inter-correlated brain recordings in the brain-computer interface. However, PLSR is in essence formulated based on the least square criterion, thus, being non-robust with respect to noises. The aim of this study is to propose a new robust implementation for PLSR. To this end, the maximum correntropy criterion (MCC) is used to propose a new robust variant of PLSR, called as Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point approach. We evaluate the proposed PMCR with a synthetic example and the public Neurotycho electrocorticography (ECoG) datasets. The extensive experimental results demonstrate that, the proposed PMCR can achieve better prediction performance than the conventional PLSR and existing variants with three different performance indicators in high-dimensional and noisy regression tasks. PMCR can suppress the performance degradation caused by the adverse noise, ameliorating the decoding robustness of the brain-computer interface.