[1]王星星,邵杰,陈鑫,等.基于改进的GAF算法的EEG情感识别[J].计算机技术与发展,2024,34(05):109-116.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0048]
　WANG Xing-xing,SHAO Jie,CHEN Xin,et al.Emotion Recognition of EEG Signals Based on Improved GAF Algorithm[J].,2024,34(05):109-116.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0048]
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基于改进的GAF算法的EEG情感识别()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年05期
页码:
109-116
栏目:
人工智能
出版日期:
2024-05-10

文章信息/Info

Title:
Emotion Recognition of EEG Signals Based on Improved GAF Algorithm
文章编号:
1673-629X(2024)05-0109-08
作者:
王星星; 邵杰; 陈鑫; 杨世逸林; 杨鑫
南京航空航天大学 电子信息工程学院,江苏 南京 210016
Author(s):
WANG Xing-xing; SHAO Jie; CHEN Xin; YANG Shi-yi-lin; YANG Xin
School of Information and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
关键词:
脑电图; 情感识别; 格拉姆角场; 马氏距离; 卷积神经网络
Keywords:
electroencephalography (EEG); emotion recognition; Gramian angular field (GAF); Mahalanobis distance; convolutional neural network (CNN)
分类号:
TP391.4;R318;TP18
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0048
摘要:
利用脑电图(EEG)信号对人类的情感进行识别一直是一个重要且具有挑战性的研究领域。 传统的方法都是对一维 EEG 信号进行分析,然后提取特征进行识别;但这种方法需要提取许多时域或频域上的特征才能取得较好的识别效果。考虑到二维图像蕴含的信息要远远比一维信号蕴含的信息丰富,因此将一维信号转换成二维图像可以提取更加有效的特征进行识别。 为此,该文提出了一种基于改进的 Gramian Angular Field(GAF)算法的 EEG 情感识别方法。 首先,从 EEG 信号中提取 alpha、beta、gama 三个频段的子带信号;然后,提出了一种基于马氏距离加权的改进 GAF 算法将一维 EEG 信号转换成二维特征图像;接着,从二维图像中提取奇异值熵、图能量等特征;最后,利用卷积神经网络(CNN)对提取的 EEG 特征进行分类识别。 基于广泛使用的 DEAP 数据集,针对四分类(HAHV、LAHV、LALV 和 HALV)情感识别任务,对该模型进行了验证。 实验结果表明:所提算法的平均分类准确率达到 92. 63% ,与现有的识别方法对比具有一定的优势。
Abstract:
Human emotion recognition using electroencephalography (EEG) signals is an important and challenging research area. The traditional method is to analyze the one-dimensional EEG signal,and then extract features for identification. But this method needs to extract many features in time domain and frequency domain to achieve better identification effect. Considering that the information contained in two-dimensional images is much richer than that contained in one-dimensional signals,converting one-dimensional signals into two-dimensional images can extract more effective features for recognition. We propose an EEG emotion recognition method based on an improved Gramian Angular Field (GAF) algorithm. First,the sub-band signals of alpha,beta,and gama are extracted from the EEG signal. Then,an improved GAF algorithm based on Mahalanobis distance weighting is proposed to convert the one-dimensional EEG signal into a two-dimensional featured images,and features such as singular value entropy and graph energy are extracted from the two-dimensional featured images. Finally,convolutional neural network (CNN) is used to classify the extracted EEG features. Based on the widely used DEAP dataset,the model is validated for the four-class (HAHV,LAHV,LALV,and HALV) emotion recognition task.The experimental results show that the average classification accuracy of the proposed algorithm reaches 92.63%,which has certain advantages compared with the existing recognition methods.

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更新日期/Last Update: 2024-05-10