基于卡尔曼滤波的ECoG信号去噪方法研究

脑电信号反映了生物体的大脑活动,在采集和处理过程中极易受到各种噪声的干扰,如眨眼、快速眼动、心电、肌电等,这些噪声给脑电信号的分析处理带来了很大的困难。本文提出了卡尔曼滤波模型和模型参数估计的方法,将其应用于脑电ECoG信号去噪预处理。实验所用的数据是公开的脑机接口竞赛实验数据（BCI Competition Ⅲ dataset Ⅰ）,分类正确率为92%。实验结果表明通过本方法去噪预处理后,分类正确率比竞赛第一名高,并且优于小波去噪与谱减法等预处理方法。     

Determination of ECoG information flow activity based on Granger causality and Hilbert transformation

Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm × 8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8 × 8 (0.016–300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.     

Early changes in apparent diffusion coefficient of rat brain following total circulatory arrest

The apparent diffusion coefficient (ADC) of rat brain was determined for the cortex [(771±23)×10^–6 m²/s] and caudate-putamen (CP) [(601±25)×10^–6 m²/s]. Using the ultrafast imaging technique U-FLARE changes in ADC were followed with a 2.4-min temporal resolution after the induction of total circulatory arrest by intravenous KC1 injection. For both tissue types, a biphasic decrease of ADC was observed. The initial fast phase led to an ADC decrease by (27±4)% (cortex) and (29±3)% (CP) within 5 min, whereas the slow continuous decrease of the second phase resulted in (68±3)% (cortex) and (66±3)% (CP) of control after 18 min. The similar relative reduction in ADC for the cortex and the CP meant that an effective distinction between both tissue types persisted after the cessation of systemic and cerebral blood flow.Address for correspondence: Max-Planck Institut für neurologische Forschung, Gleuelerstrasse 50, D-50931 Köln, Germany. Additional reprints of this chapter may be obtained from the Reprints Department, Chapman & Hall, One Venn Plaza, New York, NY 10119.     

基于频带能量归一化和SVM-RFE的ECoG分类

针对基于运动想象(左手小手指和舌头)的皮层脑电(electrocortieographic,ECoG)信号的分类问题,对BC12005竞赛数据集Ⅰ中的ECoG信号使用频带能量(band power,BP)归一化算法提取运动相关电位(movement related potential,MRP)、μ节律和β节律的频带能量作为特征.针对特征提取后维数较高的问题,使用基于支持向量机的回归特征消去(support vector machinerecursive feature elimination,SVM-RFE)算法进行特征选择,通过对训练数据集使用lO段交叉验证(cross validation,CV)的方法寻找最佳特征组合,确定特征在维数为6时具有最低平均识别错误率,对测试数据集采用同样的方法和同样的组合进行特征提取,并使用线性支持向量机进行分类,分类正确率可以达到93%.     

利用频带能量和k近邻分类器进行皮层脑电图分类

脑-机接口系统是一个不依靠外周神经和肌肉而实现大脑和外部设备之间进行直接的交流和控制的通道。对一个典型的采用皮层脑电图的植入式脑-机接口系统进行了离线分析。首先,采用频带能量特征进行导联的选择,从64导联中获取特征最明显的11导进行分析;然后,采用采用频带能量对11导皮层脑电图进行特征提取,得到22维的特征矢量;最后,采用采用k近邻分类器对两类意识任务(想象左手小手指运动或舌头运动)进行分类。离线分析结果表明,该方法对测试数据取得了很好的分类准确率。