[1]王励烨,丁威威.基于同步性脑网络的注意力识别研究[J].计算机技术与发展,2023,33(02):146-152.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 022]
　WANG Li-ye,DING Wei-wei.Attention Recognition Based on Synchronous Brain Network[J].,2023,33(02):146-152.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 022]
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基于同步性脑网络的注意力识别研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年02期
页码:
146-152
栏目:
人工智能
出版日期:
2023-02-10

文章信息/Info

Title:
Attention Recognition Based on Synchronous Brain Network
文章编号:
1673-629X(2023)02-01146-07
作者:
王励烨; 丁威威
南京邮电大学 电子与光学工程学院 微电子学院,江苏 南京 210023
Author(s):
WANG Li-ye; DING Wei-wei
School of Electronic and Optical Engineering & Microelectronics,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
注意力分类; 卷积神经网络; 特征选择; 锁相值; 信息增益
Keywords:
attention classification; convolutional neural network; feature selection; phase locking value; information gain
分类号:
TP183;TN911. 8
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 02. 022
摘要:
检测人类大脑的注意力状态是脑机接口技术的一个研究热点。 从 EEG( Electroencephalogram) 脑网络的视角来探寻注意力的形成机理并进行分类研究,提出了一种基于同步性脑网络和信息增益的注意力分类算法( SBN-IG) 。 该算法采用锁相值( Phase Locking Value,PLV) 构建脑网络提取大脑信息的耦合关系,同时为了保留脑网络结构的信息,采用卷积神经网络作为分类器,构建了一种基于同步性脑网络的注意力分类算法(SBN) ,分类准确率达到了 90. 09% 。 为了后续的应用需求,对 SBN 增加了基于信息增益的特征稀疏算法,以充分提取脑网络的重要连接信息,降低特征的冗余度,构成算法 SBN-IG。 结果表明,特征稀疏后使用 13 个电极就能够实现 86. 88% 的分类准确率,同时提升了算法效率,降低了运算量,为实时检测注意力设备研发提供了算法基础。
Abstract:
Detection attention state of human brain is a hot topic in Brain-Computer Interface ( BCI) technology. The physiological manifestation of attention from the perspective of Electroencephalogram brain network is explored and classification research is conducted. Analgorithm of Synchronous Brain Network and Information Gain ( SBN-IG) for attention classification is proposed. This algorithm usesPhase Locking Value ( PLV) to construct a synchronous brain network and extract the coupling of brain information. To preserve thestructure information of brain networks,with the Convolutional Neural Network as the classifier,an attention classification algorithm basedon SBN is proposed. The classification accuracy of SBN - IG can reach 90. 09% . For subsequent application requirements, a featuresparse algorithm based on information gain is added to the SBN to extract the important connection information of the brain network andreduce the redundancy of features, which constitute the SBN - IG. As a result, the classification accuracy can reach 86. 88% by 13electrodes. The efficiency of the algorithm is improved and the computation is reduced. The algorithm provides a theoretical basis for theresearch of real-time state detection equipment.

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更新日期/Last Update: 2023-02-10