从局部到全局脑电感知模式的研究

发布时间：2018-04-08 18:41

本文选题：脑电感知模式　切入点：总体经验模态分解　出处：《浙江大学》2017年博士论文

【摘要】：大脑是目前人们已知的结构和功能最复杂的系统,而头皮脑电由于其无损、方便和成本低的优势成为研究大脑活动的一种有效手段。本文通过头皮脑电信号研究了大脑局部和全局的感知模式。提出了一种基于总体经验模态分解和递归定量分析的单通道脑电模式识别算法,用于分析大脑的局部感知模式。将该算法应用于单通道缺氧脑电模式的研究时,不仅能检测到大脑早期缺氧还可以通过脑电模式描述缺氧程度的变化。由于大脑在受到感觉刺激或进行认知活动时,可能需要不同区域间的协同工作,单个局部区域的信息无法描述整个感知模式。因此从单通道扩展到多通道脑电信号的研究。从局部模式扩展到全局模式的分析时要提取到不同区域的共有模式信息,因此将经验模态分解的多变量扩展应用于多通道全局脑电模式分析中。本文又提出了基于多变量经验模态分解的多通道脑电模式识别算法。应用多变量经验模态分解提取出多个通道的共有模式再分析其同步性,可以更清晰地描述缺氧环境中全局脑功能网络连通性的变化。在研究手指痛觉刺激的脑电时空模式时,使用高密度电极阵列大大提高了脑电信号的空间分辨率。多变量经验模态分解的使用既避免了对载波段繁琐的优化过程,又降低了被试个体差异的影响。特征提取算法改进后更适用于头皮脑电信号的分析,可以更好地区分不同手指受刺激后的脑电时空模式。
[Abstract]:The brain is the system structure and function are known to the most complex, and scalp EEG due to its nondestructive, convenient and low cost advantage has become an effective way to study brain activity. The local and global brain research by scalp EEG patterns of perception. This paper proposed an analysis based on ensemble empirical mode decomposition quantitative and recursive pattern recognition algorithm for single channel EEG, local perception mode analysis of the brain. The studying algorithm applied in single channel EEG hypoxia model, not only can detect early brain hypoxia can also describe the change pattern of EEG. Due to brain hypoxia by sensory stimulation or cognitive activities when may need to work between different areas, individual local area information can not describe the whole perception mode. So from the single channel extended to multi channel EEG signal Analysis from the local model is expanded to a global model to extract to different areas of the common mode of information, so the multivariate empirical mode decomposition is applied to the global multi channel EEG pattern analysis. This paper proposes multivariate empirical mode decomposition of multi channel EEG pattern recognition algorithm based on the application. Multivariate empirical mode decomposition to extract multiple channels of common mode analysis of the synchronization, we can clearly describe the global hypoxia of brain functional network connectivity changes in the EEG space-time pattern of finger pain stimulus when using high density electrode array greatly improves the spatial resolution of EEG signals. Multivariate empirical mode decomposition using not only avoids the optimization of the carrier band cumbersome, but also reduces the influence of subjects of individual differences. Feature extraction is more suitable for the scalp EEG improved algorithm The analysis of the signal can better distinguish the spatial and temporal patterns of brain electricity after the stimulation of different fingers.

【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：R338;TN911.7

【相似文献】