基于相关熵的自动相关性确定逻辑回归用于鲁棒稀疏脑活动解码
Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding
AI总结：
针对高维脑活动信号中噪声导致稀疏分类性能下降的问题，提出基于相关熵的鲁棒稀疏逻辑回归算法，在合成、EEG和fMRI数据集上验证了更高准确率和更具信息量的特征选择。
摘要：
近年来的研究利用稀疏分类方法从高维脑活动信号中预测分类变量，以揭示人类的意图和心理状态，并在模型训练过程中自动选择相关特征。然而，现有的稀疏分类模型可能容易受到脑记录中固有噪声导致的性能下降的影响。为解决这一问题，本研究旨在提出一种新的鲁棒稀疏分类算法。为此，我们将相关熵学习框架引入基于自动相关性确定的稀疏分类模型中，提出了一种新的基于相关熵的鲁棒稀疏逻辑回归算法。为证明所提算法优越的脑活动解码性能，我们在一个合成数据集、一个脑电图（EEG）数据集和一个功能性磁共振成像（fMRI）数据集上对其进行了评估。大量实验结果表明，所提方法不仅能在噪声和高维分类任务中实现更高的分类准确率，而且能为解码场景选择更具信息量的特征。将相关熵学习方法与自动相关性确定技术相结合，将显著提高对噪声的鲁棒性，从而产生更充分的鲁棒稀疏脑解码算法。它为现实世界中的脑活动解码和脑机接口提供了一种更强大的方法。
英文摘要：
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.
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