基于二维集合经验模式分解的稳态视觉诱发电位目标检测研究  

Study on Steady State Visual Evoked Potential Target Detection Based on Two-dimensional Ensemble Empirical Mode Decomposition

作　　者：杨晨[1] 黄丽亚[1] 文念 杨俊宇[1] 

机构地区：[1]南京邮电大学电子科学与工程学院,南京210003

出　　处：《生物医学工程学杂志》2015年第3期508-513,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金项目资助(61003237);江苏省高校自然科学研究项目资助(10KJB510018)

摘　　要：稳态视觉诱发电位(SSVEP)是由持续的视觉刺激而诱发的节律性脑电信号。SSVEP频率由固定的视觉刺激频率及其谐波频率组成。二维集合经验模式分解(2D-EEMD)是经典的经验模式分解算法的改进算法,将分解拓展到二维方向上。本文首创性地将2D-EEMD应用于SSVEP。分解得到的本征模式函数(IMF)的二维图像可清晰地观测到SSVEP频率。经过噪声和伪迹滤除的SSVEP主要有效IMF成分投影到头图上,可以反映大脑对视觉刺激的时变趋势,以及大脑不同区域的反应程度,结果显示枕叶区对于视觉刺激的反应最为强烈。最后本文用短时傅里叶变换(STFT)对2D-EEMD的重构信号进行SSVEP频率提取,其识别准确率提高了16%。Brain computer interface is a control system between brain and outside devices by transforming electroen- cephalogram （EEG） signal. The brain computer interface system does not depend on the normal output pathways, such as peripheral nerve and muscle tissue, so it can provide a new way of the communication control for paralysis or nerve muscle damaged disabled persons. Steady state visual evoked potential （SSVEP） is one of non-invasive EEG signals, and it has been widely used in research in recent years. SSVEP is a kind of rhythmic brain activity simulated by continuous visual stimuli. SSVEP frequency is composed of a fixed visual stimulation frequency and its harmonic frequencies. The two-dimensional ensemble empirical mode decomposition （2D-EEMD） is an improved algorithm of the classical empirical mode decomposition （EMD） algorithm which extended the decomposition to two-dimensional direction. 2D-EEMD has been widely used in ocean hurricane, nuclear magnetic resonance imaging （MRI）, Lena im- age and other related image processing fields. The present study shown in this paper initiatively applies 2D-EEMD to SSVEP. The decomposition, the 2-D picture of intrinsic mode function （IMF）, can show the SSVEP frequency clearly. The SSVEP IMFs which had filtered noise and artifacts were mapped into the head picture to reflect the time changing trend of brain responding visual stimuli, and to reflect responding intension based on different brain regions. The results showed that the occipital region had the strongest response. Finally, this study used short-time Fourier transform （STFT） to detect SSVEP frequency of the 2D-EEMD reconstructed signal, and the accuracy rate increased by 16%.

关 键 词：脑机接口 视觉稳态诱发电位 二维集合经验模式分解 本征模式函数 短时傅里叶变换 准确率 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]