临床前轻度认知障碍脑电信号的耦合同步特征提取与分类研究

项目名称： 临床前轻度认知障碍脑电信号的耦合同步特征提取与分类研究

项目编号： No.61503326

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 文冬

作者单位： 燕山大学

项目金额： 21万元

中文摘要： 最新研究表明大脑皮质神经网络间相互作用的异常是临床前轻度认知障碍发生和发展的主要表现，这为研究该疾病的诊断提供了新机遇。可靠地提取头皮脑电信号以及溯源后脑皮层脑电信号的局部耦合与全局同步特征并设计与之有效搭配的分类器，是诊断临床前轻度认知障碍的关键问题。本项目针对被试者多脑区两两通道耦合强度与方向特征提取后存在维数偏高的问题，拟借助线性判别分析改进排序条件互信息方法；考虑到被试者脑电信号的分析对全局同步方法的计算准确度存在较高的要求，拟研究条件全局耦合指数同步方法；由于当前分类方法无法有效满足被试者临床诊断的需求，拟探索概率判决快速学习网方法；结合临床数据，集成耦合、同步特征提取与分类方法，将他们用于临床前轻度认知障碍及对照组脑电信号的分析，并比较这些算法的性能，验证算法的临床价值。该项目的开展，将为研究临床前轻度认知障碍的诊断奠定技术基础，有助于深入理解临床前轻度认知障碍的生理机制。

中文关键词： 认知障碍；脑电/脑磁图；神经信息解码；认知功能的脑网络表征；脑电信号分类

英文摘要： The newest studies suggested that the abnormalities of the interaction between cortical neural network were main performances of the occurrence and development of preclinical mild cognitive impairment (Pre-MCI), and the result provides new opportunities for the diagnosis of the disease. Reliably extracting local coupling and global synchronization characteristics of scalp EEG signals and the sourcing cerebral cortex EEG signals, and designing the classifier matching effectively with the characteristics, is the key problem in diagnosing Pre-MCI. For the existed problem of higher dimension after extracting features of coupling strength and direction between two channels from multiple brain regions of subjects, this project plans to improve permutation conditional mutual information with the help of linear discriminant analysis; considering that the analysis of Pre-MCI EEG signal showed higher request on the calculation accuracy of global synchronization method, this project intends to explore the synchronization method named conditional global coupling index; on account of the fact that current classification methods can not meet the clinical diagnostic requirements of subjects effectively, the project plans to explore the probabilistic discriminative fast learning network method; combining with clinical data, and integrating the methods of coupling, synchronization feature extraction and classification, use them to analyze the EEG signals of Pre-MCI and control groups, compare the performance of these algorithms, and verify the clinical value of the algorithms. The project will establish the technical foundation for the diagnosis of Pre-MCI, and help us to further understand the physiological mechanism of Pre-MCI.

英文关键词： Cognitive impairment;EEG/MEG;Neural information coding;Brain network for representation of cognitive function;Classification of EEG signals