Recent studies have utilized sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's intentions and mental states, selecting the relevant features automatically in the model training process. However, existing sparse classification models will likely be prone to the performance degradation which is caused by noise inherent in the brain recordings. To address this issue, we aim to propose a new robust and sparse classification algorithm in this study. To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm. To demonstrate the superior brain activity decoding performance of the proposed algorithm, we evaluate it on a synthetic dataset, an electroencephalogram (EEG) dataset, and a functional magnetic resonance imaging (fMRI) dataset. The extensive experimental results confirm that not only the proposed method can achieve higher classification accuracy in a noisy and high-dimensional classification task, but also it would select those more informative features for the decoding scenarios. Integrating the correntropy learning approach with the automatic relevance determination technique will significantly improve the robustness with respect to the noise, leading to more adequate robust sparse brain decoding algorithm. It provides a more powerful approach in the real-world brain activity decoding and the brain-computer interfaces.
翻译:最近的研究利用了稀疏的分类方法,预测来自高维大脑活动信号的绝对变量,以暴露人类的意图和精神状态,自动选择模型培训过程中的相关特征。然而,现有的稀疏分类模型可能容易因大脑记录中固有的噪音而导致性能退化。为解决这一问题,我们的目标是在本研究中提出一种新的稳健和稀散的分类算法。为此目的,我们将科伦罗普学习框架引入基于低维分类模式的自动相关性确定模型中,提出一个新的基于correntropy的稳健分散的后勤回归算法。为了展示高级大脑活动解码功能,我们用一个合成数据集、电子脑图数据集和功能性磁共振动成像(fMRI)数据集来评估它。广泛的实验结果证实,不仅拟议的方法能够在噪音和高维度分类任务中实现更高的分类准确度,而且还为解码情景选择了更丰富的信息特征。将correntropy学习方法与自动相关性确定技术结合起来。我们用一个合成数据集、电子脑图和功能性磁性磁性磁感成像模型将大大改善大脑界面,从而进行更稳健健健健的磁的读的大脑模拟活动。