基于表面肌电分解的皮层肌肉耦合机理研究

基于表面肌电信号分解还原了肌电信号最原始的组成成分,通过分解后肌电信号段的特征研究神经肌肉系统中脑肌电信息传递规律,可以从生物电信息传递机理探索人体运动的本质.本文分别采集了9名受试者最大抓握力量的15%和30%(15%MVC、30%MVC)所对应的EEG(Electro Encephalo Graph)和sEMC(surface Electro Myo Graphy)信号,以形态学分解为基础对sEMG信号进行模板重构分解,获得运动单元动作电位MUAP(Motion Unit Action Potential),提取MUAP的幅值、数量和发射速率作为特征,基于该类特征与同步脑电信号的变化趋势以及传递熵值探索大脑皮层与肌肉的信息传递规律.不同抓握力量水平下,30%MVC提取的3个特征均比15%MVC的数值更显著,但两种力量水平提取的3个特征随同步脑电变化趋势相同:当EEG信号形成波峰或波谷信号时,MUAP数量、幅值和发射速率特征均呈现增加的变化趋势,其中MUAP幅值的增加趋势最为明显,且MUAP幅值特征与同步EEG信号的耦合(TE传递熵值)效果最好.虽然力量水平的不同会影响脑肌电信号强弱,但总体呈现的信息传递规律是一致的:当肢体肌肉收缩脑电信号增强而形成波峰或波谷时,MUAP数量、幅值和发射速率3个特征值均呈现上升的变化趋势,其中MUAP幅值特征响应效果最好,该特征能较好体现人体运动控制过程中神经肌肉系统中的信息传递规律.

Multi-face detection based on downsampling and modified subtractive clustering for color images

This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the proposed method which modifies the subtractive clustering. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. The effectiveness of the proposed method is illustrated by three experiments.