基于RLLE算法的脑力负荷分类

Mental Load Classification Based on RLLE Algorithm

近年来,随着人工智能领域技术的不断发展,脑机接口(brain-computer interface,BCI)吸引了更多学者的关注。实时监测高强度脑力工作者的脑力负荷水平并根据其任务做出动态调整是保护国家财产和操作人员安全的重要手段。研究表明由脑电图(electroencephalogram,EEG)提取的特征功率谱密度对于脑力负荷的变化比较敏感,但由于其维数过高,容易造成数据灾难。传统的主成分分析降维(principal component analysis,PCA)算法会损失数据的部分非线性特征。局部线性嵌入(locally linear embedding,LLE)是常用的非线性降维算法,但该算法对噪声的敏感性高,降维结果受参数影响较大。稳健局部线性嵌入算法RLLE(robust locally linear embedding),在LLE优化权重矩阵时添加了正则项优化,不仅增强了模型的抗噪能力,也解决了解模型过程中可能会出现的矩阵病态和奇异性问题。实验结果表明,经过RLLE降维后的数据使用支持向量机(support vector machine,SVM)分类精度普遍高于经过PCA和LLE的降维方式,具有更强的抗干扰能力。

In recent years,with the continuous development of technology in the field of artificial intelligence,brain-computer interface(BCI)has attracted more scholars attention.Real-time monitoring of the mental load level of high-intensity mental workers and making dynamic adjustment according to their tasks is an important means to protect national property and the safety of operators.Studies have shown that the characteristic power spectrum density extracted by electroencephalogram(EEG)is sensitive to the change of mental load,but its dimension is too high,which is prone to data disaster.The traditional principal component analysis(PCA)algorithm will lose some of the nonlinear characteristics of the data.Locally Linear Embedding(LLE)is a commonly used nonlinear dimensionality reduction algorithm,but the algorithm is highly sensitive to noise,and the dimensionality reduction results are greatly affected by parameters.RLLE(robust locally linear embedding)adds regular term optimization when LLE optimizes the weight matrix,which not only enhances the anti-noise ability of the model,but also solves the problem of matrix sickness and singularity that may occur in the process of understanding the model.The experimental results show that the classification accuracy of the data reduced by RLLE using support vector machine(SVM)is generally higher than that of the data reduced by PCA and LLE,and has stronger anti-interference ability.