基于脑电信号的ILDB情感特征提取算法

EGG-Based ILDB Algorithm of Emotion Feature Extration

近年来,随着信号处理和机器学习技术的快速发展,基于脑电信号的情感识别越来越受到重视。特征提取是情感识别过程中的关键一步。本文提出了改进的局域判别基(Improved Local Discriminant Bases,ILDB)算法,提取信号局域判别基各子空间的能量和系数均值特征构成特征向量,利用SVM分类器进行分类,通过对特征向量类可分性及分类正确率的评估,表明ILDB算法提取的特征具有可分性且分类正确率较高。ILDB算法的通道最高平均分类正确率达到88%,通道最高平均分类正确率比LDB算法提高4.4%和7.2%,所有通道平均分类正确率比LDB算法提高10.1%和9.8%。

In recent years,with the rapid development of signal processing and machine learning technology,EEG-based emotion recognition has received more and more attention,in which feature extraction is a key step.This paper proposes an improved local discriminant bases（ILDB）algorithm,in which both the energy and the mean of each signal subspace coefficients are extracted from ILDB to construct feature vectors and SVM is utilized to classify.By assessing the separability of feature vectors and classification accuracy rate,the extracted features via ILDB are separable and have higher classification accuracy.The highest average classification accuracy rate of ILDB algorithm can attain 88%,which is 4.4%and 7.2% higher than that of LDB algorithm.Moreover,the average classification accuracy rate in all channels of ILDB algorithm increases by 10.1% and 9.8%.