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Received:January 17, 2022 Revised:February 15, 2022
Received:January 17, 2022 Revised:February 15, 2022
中文摘要: 脑电信号具有动态、非线性和数值高度随机的特点, 针对传统的人工神经网络模型识别脑电信号时在特征提取和识别精度方面表现出的局限性, 本研究提出了一种新的识别方法, 使用KIV模型对脑电信号进行识别. 首先, 通过仿真实验, 分析了KIV模型不同的刺激下表现出的动力学特性. 接着, 使用KIV模型分别对癫痫脑电信号和情感脑电信号进行识别, 在实验过程中不进行特征提取, 直接将多通道原始脑电信号输入到KIV模型中, 在BONN和GAMEEMO数据集上分别获得了99.50%和90.83%的识别准确率. 研究结果表明, 与现有的模型相比, KIV模型具有较好的识别脑电信号的能力, 可为脑电识别提供帮助.
Abstract:Electroencephalography (EEG) has dynamic, nonlinear and numerically highly random signals. To break the limitations of traditional artificial neural network models in feature extraction and recognition accuracy during EEG recognition, this study proposes a new recognition method, which is based on the KIV model to recognize EEG signals. First, the dynamic characteristics of the KIV model under different stimuli are analyzed through simulation experiments. Then, the KIV model is used to recognize epileptic EEG signals and emotional EEG signals. Without feature extraction during the experiment, multi-channel raw EEG signals are directly input into the KIV model for recognition. The recognition accuracy is about 99.50% and 90.83% on BONN and GAMEEMO datasets, respectively. The results show that the KIV model outperforms existing models in the ability to recognize EEG signals and can provide help for EEG recognition.
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基金项目:湖南省科技厅高新技术产业科技创新引领计划(2020GK2009); 国防科工局国防基础科研计划(WDZC20205500119); 湖南省交通运输厅科技进步与创新计划(201927); 湖南省自然科学基金(2021JJ30456); 工业控制技术国家重点实验室开放课题(ICT2021B10)
引用文本：
刘宏,陈玲钰,韦小平,张释文,张锦.基于KIV模型的脑电识别方法.计算机系统应用,2022,31(10):356-367
LIU Hong,CHEN Ling-Yu,WEI Xiao-Ping,ZHANG Shi-Wen,ZHANG Jin.EEG Recognition Method Based on KIV Model.COMPUTER SYSTEMS APPLICATIONS,2022,31(10):356-367
刘宏,陈玲钰,韦小平,张释文,张锦.基于KIV模型的脑电识别方法.计算机系统应用,2022,31(10):356-367
LIU Hong,CHEN Ling-Yu,WEI Xiao-Ping,ZHANG Shi-Wen,ZHANG Jin.EEG Recognition Method Based on KIV Model.COMPUTER SYSTEMS APPLICATIONS,2022,31(10):356-367
