{"title":"一类神经质量模型的自适应参数识别及其在人机工程系统中的应用","authors":"S. A. Plotnikov, A. L. Fradkov","doi":"10.17587/mau.25.13-18","DOIUrl":null,"url":null,"abstract":"This paper considers one of the problems that arise in the developing of the ergatic brain-computer interfaces. This technology allows a person to control various mechatronic systems through the \"power of thought\", i.e. based on the registration of electrical activity of the brain. The problem is the complexity and poor knowledge of the brain. To describe the electrical activity of the brain, various models of neural ensembles are used, one of which is the neural mass model proposed by Jansen and Rit in 1995. To tune the parameters of this model according to real data, it is proposed to use an adaptive parameter identifier. An important condition for the synthesis of an adaptive identifier is that only the system output, which is the potential difference between two points of the head, can be measured. At the beginning, it is assumed that the entire state vector of the neural mass model is available for measurement. An identifier is synthesized to tune the parameters of such a system and its convergence is proved using the Lyapunov function method. Further, the obtained identifier is refined in such a way that it uses only the output of the system. To do this, using the finite difference method, the output derivative of the neural mass model is approximately calculated, which is used to make several replacements of the unknown components of the state vector. It is very difficult to analytically prove the convergence of the obtained adaptive parameter identifier, therefore, the possibility of using it to estimate the parameters of a neural mass model is checked using simulation. The synthesized identifier uses only the system output to tune the parameters, which in the future will allow us to consider real data instead of the system output. Thus, this identifier can be used to tune the parameters of the neural mass model based on real data.","PeriodicalId":36477,"journal":{"name":"Mekhatronika, Avtomatizatsiya, Upravlenie","volume":"12 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Parameter Identification for a Class of Neural Mass Models with Application to Ergatic Systems\",\"authors\":\"S. A. Plotnikov, A. L. Fradkov\",\"doi\":\"10.17587/mau.25.13-18\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers one of the problems that arise in the developing of the ergatic brain-computer interfaces. This technology allows a person to control various mechatronic systems through the \\\"power of thought\\\", i.e. based on the registration of electrical activity of the brain. The problem is the complexity and poor knowledge of the brain. To describe the electrical activity of the brain, various models of neural ensembles are used, one of which is the neural mass model proposed by Jansen and Rit in 1995. To tune the parameters of this model according to real data, it is proposed to use an adaptive parameter identifier. An important condition for the synthesis of an adaptive identifier is that only the system output, which is the potential difference between two points of the head, can be measured. At the beginning, it is assumed that the entire state vector of the neural mass model is available for measurement. An identifier is synthesized to tune the parameters of such a system and its convergence is proved using the Lyapunov function method. Further, the obtained identifier is refined in such a way that it uses only the output of the system. To do this, using the finite difference method, the output derivative of the neural mass model is approximately calculated, which is used to make several replacements of the unknown components of the state vector. It is very difficult to analytically prove the convergence of the obtained adaptive parameter identifier, therefore, the possibility of using it to estimate the parameters of a neural mass model is checked using simulation. The synthesized identifier uses only the system output to tune the parameters, which in the future will allow us to consider real data instead of the system output. Thus, this identifier can be used to tune the parameters of the neural mass model based on real data.\",\"PeriodicalId\":36477,\"journal\":{\"name\":\"Mekhatronika, Avtomatizatsiya, Upravlenie\",\"volume\":\"12 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mekhatronika, Avtomatizatsiya, Upravlenie\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17587/mau.25.13-18\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mekhatronika, Avtomatizatsiya, Upravlenie","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17587/mau.25.13-18","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
摘要
本文探讨了在开发人体工学脑机接口过程中出现的一个问题。这项技术允许人们通过 "思维的力量",即基于大脑电活动的注册,来控制各种机电一体化系统。问题在于大脑的复杂性和对大脑的不了解。为了描述大脑的电活动,人们使用了各种神经集合模型,其中之一是 Jansen 和 Rit 于 1995 年提出的神经质量模型。为了根据真实数据调整该模型的参数,建议使用自适应参数识别器。合成自适应识别器的一个重要条件是只能测量系统输出,即头部两点之间的电位差。在开始时,假定神经质量模型的整个状态向量都可用于测量。我们合成了一个识别器来调整这样一个系统的参数,并使用 Lyapunov 函数方法证明了它的收敛性。此外,还对所获得的识别器进行了改进,使其只使用系统的输出。为此,使用有限差分法近似计算神经质量模型的输出导数,并以此对状态向量的未知分量进行多次替换。要分析证明所获得的自适应参数识别器的收敛性非常困难,因此需要通过模拟来检验用它来估算神经质量模型参数的可能性。合成的识别器仅使用系统输出来调整参数,这将使我们将来能够考虑真实数据而不是系统输出。因此,该识别器可用于根据真实数据调整神经质量模型的参数。
Adaptive Parameter Identification for a Class of Neural Mass Models with Application to Ergatic Systems
This paper considers one of the problems that arise in the developing of the ergatic brain-computer interfaces. This technology allows a person to control various mechatronic systems through the "power of thought", i.e. based on the registration of electrical activity of the brain. The problem is the complexity and poor knowledge of the brain. To describe the electrical activity of the brain, various models of neural ensembles are used, one of which is the neural mass model proposed by Jansen and Rit in 1995. To tune the parameters of this model according to real data, it is proposed to use an adaptive parameter identifier. An important condition for the synthesis of an adaptive identifier is that only the system output, which is the potential difference between two points of the head, can be measured. At the beginning, it is assumed that the entire state vector of the neural mass model is available for measurement. An identifier is synthesized to tune the parameters of such a system and its convergence is proved using the Lyapunov function method. Further, the obtained identifier is refined in such a way that it uses only the output of the system. To do this, using the finite difference method, the output derivative of the neural mass model is approximately calculated, which is used to make several replacements of the unknown components of the state vector. It is very difficult to analytically prove the convergence of the obtained adaptive parameter identifier, therefore, the possibility of using it to estimate the parameters of a neural mass model is checked using simulation. The synthesized identifier uses only the system output to tune the parameters, which in the future will allow us to consider real data instead of the system output. Thus, this identifier can be used to tune the parameters of the neural mass model based on real data.