用于从含噪硬膜外皮层脑电信号中鲁棒解码轨迹的偏最大相关熵回归
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
AI总结：
针对偏最小二乘回归对噪声不鲁棒的问题，提出基于最大相关熵准则的偏最大相关熵回归方法，利用半二次优化和定点法求解，在含噪脑电信号解码中显著提升了预测性能与鲁棒性。
摘要：
偏最小二乘回归（PLSR）在脑机接口中展现出从相互关联的脑记录中预测连续变量的卓越能力。然而，PLSR本质上是基于最小二乘准则构建的，因此对噪声不具备鲁棒性。本研究旨在提出一种新的鲁棒PLSR实现方法。为此，采用最大相关熵准则（MCC）提出了一种新的鲁棒PLSR变体，称为偏最大相关熵回归（PMCR）。利用半二次优化计算用于降维的鲁棒投影矩阵，并通过定点方法优化回归系数。我们使用合成示例和公开的Neurotycho皮层脑电图（ECoG）数据集评估了所提出的PMCR。大量实验结果表明，在高维和含噪回归任务中，与传统的PLSR及现有变体相比，所提出的PMCR在三种不同性能指标下能取得更好的预测性能。PMCR能抑制由不利噪声引起的性能下降，改善脑机接口的解码鲁棒性。
英文摘要：
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.
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