FEATURE EXTRACTION OF MOTOR IMAGERY EEG BASED ON WAVELET TRANSFORM AND HIGHER-ORDER STATISTICS

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Feature extraction using attributed scattering center models on SAR imagery

Feature Extraction using Attributed Scattering Center Models on SAR Imagery
Michael A. Koets and Randolph L. Moses
Department of Electrical Engineering, The Ohio State University
ABSTRACT
We present algorithms for feature extraction from complex SAR imagery. The features parameterize an attributed scattering center model that describes both frequency and aspect dependence of scattering centers on the target. The scattering attributes extend the widely-used point scattering model, and characterize physical properties of the scattering object. We present two feature extraction algorithms, an approximate maximum likelihood method that relies on minimization of a nonlinear cost function, and a computationally faster method that avoids the nonlinear minimization step. We present results of applying both algorithms on synthetic model data, on XPatch scattering predictions of the SLICY test target, and on measured X-band SAR imagery.

第四章基于lp范数的线性判别分析...

2. 模式识别：在模式识别中，一些基于 l2 范数结构的方法也容易受到 Outliers 的影响，从而影响分类超平面的投影方向。本工作将基于 lp 范数的度量空间应用到线性判别分析算法中，替换原有基于 l2 范数的广义瑞利商。将发展的模式识别方法用于仿真数据、真实运动想象数据、基因数据、人脸识别数据以及模式识别标准数据库中的数据，与其他线性判别分析方法相比获得了较高的识别准确率，结果证明新发展的方法能够训练出对 Outlier 有较强抑制作用的分类器，并具有较高的普适性。关键词：脑电，脑-机接口，lp 范数，特征提取，模式识别
1. 特征提取：基于最小 2 乘估计（least squares estimation, LES）的脑电特征容易扩大 Outliers 的影响，从而扭曲特征对样本的反映。本工作中针对该问题，将基于 l1 范数的奇异值分解方法(L1-SVD)应用到共空间模式分析(Common Spatial Pattern, CSP)中，替换原有基于 SVD 分解的特征向量求解，从而实现对 Outliers 较好的抑制。仿真和真实运动想象数据的特征提取结果，证实了发展方法良好的噪声抑制能力。
School of Electronic Engineering
独创性声明
本人声明所呈交的学位论文是本人在导师指导下进行的研究工作及取得的研究成果。据我所知，除了文中特别加以标注和致谢的地方外，论文中不包含其他人已经发表或撰写过的研究成果，也不包含为获得电子科技大学或其它教育机构的学位或证书而使用过的材料。与我一同工作的同志对本研究所做的任何贡献均已在论文中作了明确的说明并表示谢意。
1. For feature extraction in BCI system, the conventional least square based methods will exaggerate the influence of outliers so as to distort the features. This dissertation used L1 norm based singular value decomposition to estimate the common spatial filters. The application to both the simulated and actual MI datases demonstrated that the proposed approach can robustly extract the related MI features for BCI system.

(整理)当代摄影测量双语教学词汇表

精品文档Glossary of《Introduction to Modern Photogrammetry》《当代摄影测量》双语教学词汇表AAbbe comparator principle阿贝比长原理aberration 像差absolute flying height 绝对航高absolute orientation 绝对定向absorption 吸收access 存取、访问accessory 附件、辅助设备accident error 偶然误差accuracy 精度、准确度accuracy assessment 精度评定acquisition 获取active remote sensing 主动式遥感adaptability 适应性adjustment 平差adjacent 邻接adjacent flight line 相邻航线adjacent area 邻接区域adjoining sheets 邻接图幅aerial camera 航空摄影机aerial photograph 航摄像片aerial photographic gap 航摄漏洞aerial photogrammetry航空摄影测量aerial remote sensing 航空遥感aerophotogrammetry 航空摄影测量aerotriangulation 空中三角测量block triangulation区域网三角测量strip triangulation航带法空中三角测量independent model triangulation独立模型法空中三角测量bundle triangulation光束法空中三角测量affine rectification 仿射纠正affined transformation 仿射变换aggregation 聚合、聚集air base 摄影基线airbone imagery 机载影像airborne sensor 机载传感器alignment 排列成行、对准algebra 代数algorithm 算法allocation 配置altimeter 测高仪altitude 高度、高程ambiguity 模糊、不定性anaglyph 互补色anaglyphical stereoscopic viewing互补色立体观察analog 模拟analog/digital conversion 模数转换analog photogrammetry 模拟摄影测量analytical aerotriangulation解析空中三角测量analytical photogrammetry 解析摄影测量analytical plotter 解析测图仪ancillary data 辅助数据angular field of view 像场角angular momentum 角动量animation 动画annotation 注释、注记annotated photograph 调绘像片aperture 光圈、孔径relative ~ 相对孔径effective ~ 有效孔径approximation 近似值、逼近archive 档案archiving 存档architectural photogrammetry建筑摄影测量archaeological photogrammetry考古摄影测量artificial intelligence 人工智能artificial target 人工标志（点）aspect 方位aspect map 坡向图assessment 评定、估价astigmatism 像散atlas 地图集atmospheric haze 大气蒙雾atmospheric refraction 大气折光atmospheric window 大气窗口atmospheric transmission 大气传输atmospheric transmissivity 大气透过率attenuation 衰减attitude 姿态attitude parameter 姿态参数attribute 属性autocollimation 自准直autocorrelation 自相关automatic triangulation自动空中三角测量azimuth angle 方位角azimuth resolution 方位角分辨率Bbackprojection 逆投影backup 备份ballistic camera 弹道摄影机ballistic photogrammetry弹道摄影测量bandwidth 波段宽barrel 圆筒、桶形失真baseline 基线base-height ratio 基-高比batch process 批处理baud rate 波特率Bayes classification 贝叶斯分类bilinear interpolation 双线性内插binary image 二值影像biomedical photogrammetry生物医学摄影测量biostereometrics 生物立体量测学black-and-white film 黑白片blinking method of stereoscopic viewing 闪闭法立体观察block adjustment 区域网平差blunder detection 粗差探测bulk processing 粗处理bundle of rays 光束boundary 边界breakline 断裂线bridging of models 模型连接brightness 亮度Ccadastral mapping 地籍制图calibration 检校camera calibration 摄影机检校carrier phrase measurement载波相位测量Cartesian coordinates 笛卡尔坐标cartography 地图学characteristic curve of photographic emulsion 感光特性曲线check point 检查点chromatic 彩色的classification 分类classifier 分类器close-range photogrammetry近景摄影测量clustering 聚类cognitive mapping 认知制图collinearity condition 共线条件collinearity equations 共线方程color enhancement 彩色增强color infrared film 彩色红外片color film 彩色片coma 彗星像差combined adjustment 联合平差comparator 坐标量测仪compensation 补偿complementary colors 互补色component 组件、分量compression 压缩computer aided mapping 机助测图computer vision 计算机视觉computer-aided cartography计算机辅助制图condition equations 条件方程confidence 置信度coverage 覆盖conformal 正形的、等角的contact printing 接触晒印content of information 信息量contour lines 等高线contour interval 等高距constraint 约束contrast enhancement 反差增强contrast coefficient 反差系数control point 控制点control photostrip 骨架航线convergent photography 交向摄影convolution operators 卷积算子coordinate grid 坐标格网coordinate system 坐标系photographic coordinate system 像平面坐标系image space coordinate system 像空间坐标系object coordinate system物方坐标系coplanarity equation 共面方程correlation efficient 相关系数corresponding image point 同名像点corresponding image rays 同名光线corresponding epipolar line 同名核线cosine transformation 余弦变换covariance 协方差covariance matrix 协方差矩阵crest 山脊、峰顶cross-section 断面cyberspace 信息空间、赛博空间cycle slip 周跳Ddata acquisitation 数据获取data compression 数据压缩data mining 数据挖掘data snooping 数据探测法data transmission 数据传输data processing 数据处理data warehouse 数据仓库datum 基准deformation 变形densitometer 密度计density slicing 密度分割depression 抑制、衰减depth of field 景深detector 探测器developing 显影diagonal matrix 对角矩阵diaphragm 光圈differential 差分differential method of photogrammetric mapping 分工法测图differential rectification 微分纠正diffraction 衍射diffusion 扩散、漫射digital/analog transform 数/模转换digital correlation 数字相关digital earth 数字地球digital image 数字影像digitizer 数字化器digitization 数字化digitized image 数字化影像digital mapping 数字测图digital mosaic 数字镶嵌digital surface model数字表面模型（DSM）digital terrain model数字高程模型（DTM）digital orthophoto map数字正射影像（DOM）digital orthoimage 数字正射影像digital photogrammetry 数字摄影测量digital raster graphic数字栅格地图（DRG）digital rectification 数字纠正digital tracing table 数控绘图桌dimensional 维one- dimensional一维的two- dimensional 二维的three- dimensional 三维的disparity 不同、差异displacement of image 像点位移distortion of lens 物镜畸变差distribution function 分布函数direct line transformation直接线性变换（DLT）direct scheme of digital rectification直接法纠正direction cosines 方向余弦discrimination 辨别、区分dispersion 分散、散射drainage 水系drawing 绘图drift angle 偏流角dynamic 动态的Eearth curvature 地球曲率earth ellipsoid 地球椭球eccentricity 偏心、偏心率edge detection 边缘检测edge enhancement 边缘增强eigenvalue 特征值eigenvector 特征向量electromagnetic spectrum 电磁波谱elements of interior orientation内方位元素elements of exterior orientation外方位元素elements of relative orientation相对定向元素elements of absolute orientation绝对定向元素elements of rectification 纠正元素emulsion 药膜encoding 编码enhancement 增强entity 实体entropy （信息）熵entropy coding 熵编码environment 环境epipolar line 核线epipolar plane 核面epipolar correlation 核线相关epipolar resampling 核线重采样epipole 核点equalization of histogram 直方图均衡equivalent vertical photograph等效竖直像片equally tilted photography 等倾摄影error circle 误差圆Ethernet 以太网expert system 专家系统ES exposure 曝光exposure station 摄站exponential 指数的exterior orientation 外部定向event 事件Ffalse color film 假彩色片false color photography 假彩色摄影false color composite 假彩色合成feature 特征feature coding 特征编码feature extraction 特征提取feature selection 特征选择fiducial marks 框标mechanical fiducial marks机械框标optical fiducial marks光学框标field curvature像场弯曲field of view 视场filtering 滤波fixing 定影flight block 摄影分区flight height （flying height）航高flight line 摄影航线flight plan of aerial photography航摄计划flight strip 航带flying height 航高absolute ~ 绝对航高relative ~ 相对航高flying trace 航迹floating mark 浮游测标flux 通量、流动focal distance 焦距focal length 焦距focal plane 焦平面format 像幅forward motion compensation （FMC）向前运动补偿Fourier transformation 傅立叶变换fractal 分数维frame camera 框幅式摄影机free net adjustment 自由网平差frequency 频率Fresnel 菲滠耳fuzzy classifier method 模糊分类法fuzzy image 模糊影像GGaussian distribution 高斯分布generalization 综合geodetic origin 大地原点generation 产生geodetic datum 大地基准geodetic database 大地测量数据库geocentric coordinate system地心坐标系geodetic origin 大地原点geodetic datum 大地基准geographic coding 地理编码geoid 大地水准面geomatics 测绘学geometric correction 几何校正geometric rectification 几何纠正geometric registration of image图像几何配准geometric model 几何模型geostationary 地球静止的geo-synchronous satellite 地球同步卫星gnomonic 球心的goniometer 测角器、测向器gradients 梯度graphic 图形的grating 格子、光栅gravity 重力grey level 灰度级grey scale 灰度级grey wedge 光契grid 格网ground nadir point 地底点gross error detection 粗差检测GPS aerotriangulation GPS空中三角测量Gruber point 标准配置点Hheight displacement 投影差high-pass filtering 高通滤波histogram equalization 直方图均衡histogram 直方图histogram specification直方图规格化histogram equalization直方图均衡化hologram photography 全息摄影hologrammetry 全息摄影测量homogeneous 均质的、齐次的homologous image point 同名像点homomorphic filtering 同态滤波horizon camera 地平线摄影机horizontal 水平的、平面的horizontal parallax 左右视差（x-parallax）horizontal parallax difference左右视差较hot spots 热点hough transformation 霍夫变换Huffman 霍夫曼hue 色度hypergraph 超图hypermedia 超媒体hyperspectral 高光谱、超光谱hypertext 超文本hypothesis 假设Iidentified photograph 调绘片index contour 计曲线illuminance of ground 地面照度image，imagery 影像image coding 影像编码image correlation 影像相关image description 影像描述image digitization 影像数字化image enhancement 影像增强image fusion 影像融合image interpretation 影像解译image matching 影像匹配image mosaic 影像镶嵌image motion compensation像移补偿image overlaying 影像复合image pyramids 影像金字塔image quality 影像质量image recognition 影像识别image registration 影像配准image resolution 影像分辨力image restoration 影像复原image motion compensation像移补偿（IMC）image segmentation 图像分割image space coordinate system像空间坐标系image transformation 图像变换image understanding 图像理解imaging equation 构像方程imaging radar 成像雷达imaging spectrometer 成像光谱仪incident angle 入射角independent model aerial triangulation 独立模型法空中三角测量indirect scheme of digital rectification 间接法纠正industrial photogrammetry工业摄影测量inertial measurement unit （IMU）惯性测量装置information extraction 信息提取infrared film 红外片infrared photography 红外摄影infrared remote sensing 红外遥感infrared scanner 红外扫描仪inner 内部的inner orientation 内定向instrument 仪器、设备integration 集成intensity 亮度interactive 交互interest point 兴趣点、有利点interferogram 干涉图interferometry 干涉测量学interior orientation 内部定向interometry SAR 干涉雷达（INSAR）interoperability 互操作interpolation 内插bilinear interpolation 双线性内插nearest-neighbor interpolation邻近像元内插invariant 不变量irradiance 辐射照度isocenter of photograph 像等角点isometric 等角、等值的isometric parallel 等比线iteration method 迭代法iteration method with variable weights选权迭代法intersection 相交inverse matrix 逆矩阵Kkey-in 键盘输入key word 关键字kinematic positioning 动态定位knickpoint 转折点、裂点Llaboratory 实验室Landsat 陆地卫星landform 地形landscape map 景观地图large format camera大像幅摄影机（LFC）latent 潜在的lateral tilt 旁向倾角（roll）lateral overlap（side overlap，side lap）旁向重叠layover 雷达图像移位least squares correlation最小二乘相关leveling of model 模型置平linear array sensor 线阵列传感器linear features 线特征linear transformation 线性变换linearization 线性化logarithmic 对数的longitudinal tilt 航向倾角（pitch）longitudinal overlap（end overlap，forward overlap）航向重叠low-pass 低通Mmagazine 暗盒magnification 放大manual 人工的manuscript map 原图map compilation 地图编辑map legend 图例map projection 地图投影map revision 地图更新mapping satellite 测图卫星marine charting 海洋测绘mathematical expectation 数学期望maximum likelihood classification最大似然分类matrix 矩阵mean square error 中误差measuring mark 测标mechanics 力学median filters 中值滤波器mesh 网、网格metadata 元数据meteosat 气象卫星minimum distance classification最小距离分类metric camera 量测摄影机microwave remote sensing 微波遥感method of least squares 最小二乘法microwave radiation 微波辐射microwave radiometer 微波辐射计modulation transfer function调制传递函数（MTF）moiré莫尔条纹monocomparator 单像坐标量测仪mount 安装、座架mosaic 镶嵌optical mosaic 光学镶嵌digital mosaic 数字镶嵌most probable value 最或然值multicollimator 多投影准直仪multiplex 多倍仪multistage rectification 多级纠正multispectral camera 多光谱摄影机multispectral photography多光谱摄影multispectral remote sensing多光谱遥感multispectral scanner多光谱扫描仪（MSS）multi-temporal analysis 多时相分析multi-temporal remote sensing多时相遥感multiplicity 多重性、相重性Nnadir point 底点navigation 导航negative 负片neighborhood method 邻元法nodal point 节点front nodal point 前节点rear nodal point 后节点neutral network 神经网络nonlinear 非线性的non-metric camera 非量测摄影机non-topographic photogrammetry非地形摄影测量normal case photography 正直摄影normal distribution 正态分布normal equation 法方程式normalization 正交化Ooblique 倾斜的oblique photography 倾斜摄影object space coordinate system物空间坐标系object spectrum characteristics地物波谱特性object oriented 面向对象observation 观测值observation equation 误差方程式occlusion 遮蔽offset 移位off-line 离线、脱机on-line 在线、联机on-line aerial triangulation联机空中三角测量one-dimensional 一维的opacity 不透明的operator 算子optical axis of lens 物镜主光轴optical rectification 光学纠正optical-mechanical rectification光机械学纠正optical projection 光学投影optical transfer function光学传递函数（OTF）orthogonal matrix 正交矩阵orientation elements 方位元素orientation point 定向点orthogonal projection 正射投影orthographic 正射的orthogonal matrix 正交矩阵orthoimage 正射影像orthophoto 正射像片orthophotomap 正射影像地图orthophoto stereomate正射影像立体配对片orthophoto technique 正射影像技术outline map 略图outstanding point 明显地物点overlap 重叠Ppackage 包panchromatic film 全色片panoramic camera 全景摄影机panoramic photography 全景摄影panoramic distortion 全景畸变parallax 视差parallax difference 视差较parallel-averted photography等偏摄影parameter 参数parameter estimation 参数估计pass point 加密点pattern recognition 模式识别perceived model 视模型perigee 近地点perspective center 透视中心phase transfer function相位传递函数（PTF）photogrammetric distortion摄影测量畸变差photogrammetric workstation摄影测量工作站photogrammetry 摄影测量terrestrial ~ 地面摄影测量two-medium ~ 双介质摄影测量biomedical ~ 医学摄影测量photography 摄影学photographic baseline 摄影基线photographic bundle of rays 摄影光束photographic coordinate system摄影测量坐标系photographic interpolation摄影测量内插photographic paper 相纸photographic processing 摄影处理photographic scale 摄影比例尺photo base 像片基线photo coordinate system像平面坐标系photo interpretation 像片判读photo map 像片平面图photo mosaic 像片镶嵌photo nadir point 像底点photoplan 像片平面图photo rectification 像片纠正photo scale 像片比例尺phototheodolite 摄影经纬仪physiological parallax 生理视差picture format 像幅pinhole 小孔（成像）pixel 像元planarity 平面性、平面条件platform 平台platen 压平板、平台plot 平面图、略图plumb line 铅垂线point marking 刺点point transfer 转点point of interest 兴趣点polar 极、极地的polar coordinates 极坐标polarized 极化polarization 极化polygon 多边形polynomial 多项式positive 正片power spectrum 功率谱precision 精密度precision estimation 精度估计prediction 预测、推估prick point 刺点primary color 原色principal component transformation 主分量变换principal distance of photo 像片主距principal distance 主距principal line 像主纵线principal plane 像主垂面principal point 像主点principal point of photograph 像主点principle of geometric reverse几何反转原理printer 印相机prism 棱镜precision estimation 精度估计probable error 或然误差probability 概率论processing 处理bulk processing 粗处理precision processing 精处理product 产品production 生产、产量projection 投影projection center 投影中心projection printing 投影晒印propagation of errors 误差传播protocol 协议prototype 原型pseudo-color image 伪彩色影像pseudo range measurement 伪距测量pushbroom imaging 推扫式成像pyramids 金字塔Qquadtree 四叉树qualitative 定性的quality control 质量控制quantitative 定量的quantizing 量化quantization 量化quantum 量子query 查询、检索Rradargrammetry 雷达图象测量radial distortion 径向畸变radial triangulation 辐射三角测量radiant 辐射的radiation correction 辐射校正radiograph X光照相radiometry 辐射测量radiometric correction 辐射校正radiometer 辐射计random error 随机误差、偶然误差random variable 随机变量raster grid 栅格网raster to vector conversion栅格-矢量转换ratio transformation 比值变换real-aperture radar 真实空径雷达real-time photogrammetry实时摄影测量reconstruction 重建rectifier 纠正仪rectification 纠正affine rectification 仿射纠正reduction 归化redundancy 余redundant information 余信息refinement 改正reflectance spectrum 反射波谱region of target 目标区region of search 搜索区relative flying height 相对航高relative orientation 相对定向relaxation 松池reliability 可靠性relief displacement 投影差resampling 重采样remote sensing 遥感aerial remote sensing 航空遥感space remote sensing 航天遥感remote sensing of resources 资源遥感environmental remote sensing环境遥感geological remote sensing 地质遥感ocean remote sensing 海洋遥感forest remote sensing 森林遥感atmospheric remote sensing大气遥感infrared remote sensing 红外遥感microwave remote sensing 微波遥感multi-spectral remote sensing多光谱遥感active remote sensing 主动遥感passive remote sensing 被动遥感remote sensing platform 遥感平台representation 显示、表达reseaux 网格resection 后方交会residual 残差resolution 分解力、分辨率ground resolution 地面分解力space resolution 空间分辨率temporal resolution 时间分辨率temperature resolution 温度分辨率resolving power of lens 物镜分辨力restitution 测图、成图、复原、恢复restoration 恢复retrieval 检索return beam vidicon camera反束光导（RBV）管摄影机reversal film 反转片roam 漫游rotation matrix 旋转矩阵route 路径Ssampling 采样sampling interval 采样间隔satellite altimetry 卫星测高satellite attitude 卫星姿态satellite-borne sensor 星载遥感器saturation 饱和度scaling of model 模型缩放scanner 扫描仪searching area 搜索区seasat 海洋卫星segmentation 分割self-calibration 自检校semiconductor 半导体semi-metric camera 半量测摄影机sensitivity 感光度sensitometry 感光测定sensitization 感光sensitometry 感光度测定sensitive material 感光材料sensor 传感器sequential 序列的shadow 阴影shutter 快门sidelap 旁向重叠side-looking radar侧视雷达（SLR）similarity 相似、相似性simulation 模拟single image 单张像片singularity 奇异性small format aerial photography小像幅摄影space intersection 空间前方交会space photography 航天摄影space photogrammetry航天摄影测量space remote sensing 航天遥感space resection 空间后方交会Spacelab 空间实验室space shuttle 航天飞机spatial 空间的spatial domain 空间域specification 规范、说明spectral 光谱的spectral sensitivity 光谱感光度spectrograph 摄谱仪spectrometer 波谱测定仪spectroradiometer 光谱辐射仪spectrum character curve波谱特征曲线spectrum response curve波谱响应曲线spectrum feature space 波谱特征空间sphere 球面、球体spline 样条squint 斜视static 静态的stellar camera 恒星摄影机standard deviation 标准差standard error 标准差statistical 统计的statoscope 高差仪stereocamera 立体摄影机stereocomparator 立体坐标量测仪stereometer 立体量测仪stereo pair 立体像对stereo plotter 立体测图仪stereoscope 立体镜bridge-type ~ 桥式立体镜mirror ~ 反光立体镜stereoscopic vision 立体视觉stereoscopic observation 立体观测stereopair 立体像对stereophotogrammetry立体摄影测量stereoscopic model 立体观测模型stop-number 光圈号数stochastic 随机的strips 航线、航带strip aerial triangulation航带法空中三角测量sub pixel 子像素sun-synchronous satellite太阳同步卫星superimposition 叠加supervised classification 监督分类surface model 表面模型survey adjustment 测量平差survey mark 测量标志surveying and mapping 测绘surveying 测量学elementary surveying 普通测量topographic survey 地形测量control surveying 控制测量sweep 扫描swing angle 像片旋角（yaw）symmetry 对称synthetic aperture radar合成空径雷达system integration 系统集成systematic error 系统误差Ttangential distortion 切向畸变target area 目标区template 模板terrestrial camera 地面摄影机terrestrial photogrammetry地面摄影测量texture enhancement 纹理增强texture analysis 纹理分析thematic map 专题地图thematic mapper 专题制图仪（TM）theodolite 经纬仪thermal radiation 热辐射thermal infrared imagery 热红外影像threshold 阈值tie point 连接点tilt angle of photograph 像片倾角tilt displacement 倾斜位移tracing 跟踪transparent negative 透明负片transparent positive 透明正片triangulated irregular network不规则三角网（TIN）triple 三倍的、三重的true-orthophoto 真正射影像two-medium photogrammetry toning 调色topographic map 地形图topology 拓扑toponomastics, toponymy 地名学trainning field 训练区transmittance 透光率translation 平移、移动transparent 透明的transverse 横轴、横向的triangulation 三角测量aerial ~ 空中三角测量analogue aerial ~ 模拟法空三测量analytical aerial ~ 解析法空三测量block ~ 区域网空中三角测量strip ~ 航带法空中三角测量independent model ~独立模型法空中三角测量bundle ~ 光束法空中三角测量trichromatic 三色的Uuncertainty 不确定性underwater camera 水下摄影机under photogrammetry水下摄影测量universal method of photogrammetric unit matrix 单位矩阵unit weight 单位权unsupervised classification非监督分类update 更新urban mapping 城市制图user interface 用户界面mapping 全能法测图Vvanishing point 灭点、合点variance 方差variance-covariance 方差-协方差vectograph method of stereoscopic viewing 偏振光立体观察vector 矢量vectorization 矢量化verifiability 置信度verification 确认vertical 竖直的、高程的vertical exaggeration 高程扩张vertical parallax上下视差（y-parallax）vertical photography 竖直摄影viewpoint 视点virtual reality 虚拟现实visual 目视的visual interpretation 目视判读visualization 可视化voxel 体素Wwavelet 小波wavelength 波长weight 权weight function 权函数weight matrix 权矩阵weighted mean 加权平均数whiskbroom 横扫式workstation 工作站XX-ray photogrammetry X射线摄影测量Yyan angle 航偏角y-tilt 航向倾角Zzenith angle 天顶角zonal rectification 分带纠正zone 带zone generation 区域增长zoom 缩放zoom in 缩小zoom out 放大注：更详细的摄影测量与遥感专业词汇请查阅：1、《英汉测绘词汇》. 测绘出版社2、《测绘学名词》. 测绘出版社, 2002缩写词CAC Computer-aided Cartography 机助地图制图CCD Charge-coupled Device 电荷偶合器件DCBD Digital Cadastral Database 数字地籍数据库DLG Digital Line Graph 数字线划图DRG Digital Raster Graphics 数字栅格图DOQ Digital Orthophoto Quadrangle 数字正射影像图DPW Digital Photogrammetric Workstation摄影测量工作站GLONASS Global Orbiting Navigation Satellite System [俄罗斯]全球轨道导航卫星系统GPS Global Positioning System 全球定位系统ERTS earth resources technology satellite 地球资源卫星ETM Enhancement Thermatic Mapper 增强型专题制图仪HRSC High Resolution Stereo CameraIFOV Instantaneous Field of View 瞬时视场IFSAR Interometry SAR干涉雷达IMU Inertial Measurement Unit 惯性测量装置INS Inertial Navigation System 惯性导航系统ISS Inertial Surveying System 惯性测量系统LIDAR Light Detection and Ranging 激光探测和测距LIS Land Information System 土地信息系统MTF Modulation Transfer Function 调制传递函数NDVI Normalized Difference Vegetative IndexNSDI National Spatial Data Infrastructure 国家空间数据基础设施RMSE root mean square error 均方根差，中误差SAR Synthetic Aperture Radar 合成空径雷达SDI Spatial Data Infrastructure 空间数据基础设施SLAR Side Looking Airborne Radar 侧视雷达WGS84 World Geodetic System for 1984 1984年世界大地坐标系学会、组织名称ACSM American Congress on Surveying and Mapping 美国测绘学会ASPRS American Society for Photogrammetry and Remote Sensing美国摄影测量与遥感学会CSGPC Chinese Society of Geodesy, Photogrammetry and Cartography 中国测绘学会ESA European Space Agency 欧洲空间局FIG Federation International of Geometres 国际测量师联合会ICA International Cartographic Association 国际制图协会ISO International Organization for Standardization 国际标准化组织ISPRS International Society for Photogrammetry and Remote Sensing国际摄影测量与遥感学会IUSM International Union of Surveying and Mapping 国际测量联合会NASA National Aeronautics and Space Administration [美国]国家航空与航天局NASDA National Space Development Agency [日本]国家宇宙开发事业团NGCC National Geomatics Center of China [中国]国家基础地理信息中心。

外文翻译

外文翻译毕业设计题目：植物叶片的形状特征提取及分类研究原文1： Shape based leaf image retrieval译文1：基于形状的叶片图像检索原文2：Feature extraction and automatic recognitionof plant leaf using artificial neural network 译文2：利用人工神经网络实现植物叶片的特征提取与自动识别Shape based leaf image retrievalZ. Wang, Z. Chi and D. FengAbstract: The authors prescnt an efficient two-stage approach for leaf image retrieval by using simple shape features including centroid-contour distance (CCD) curve, eccentricity and angle code histogram (ACH). In thc first stage, the images that are dissimilar with the query image will be first filtcred out by using ecccutricicy to reduce the search space, and fine retrieval will follow by using all three sets of features in the reduced search space in the second stage. Different from eccentricity and ACH, the CCD curve is ncither scaling-invariant nor rotation-invariant.Therefore, normalisation is required for the CCD curve to achieve scaling invariance, and starting point location is required to achieve rotation invariance with the similarity measure of CCD curves. A thinning-based method is proposed to locate starting points of leaf image contours, so that the approach used is more computationally cfficient. Actually, the method can bencfit other shape representations that are sensitive to starting points by reducing the matching timc in image recognition and retrieval. Experimental results on 1400 leaf images from 140 plants show that the proposed approach can achieve a better retrieval performance than both the curvature scale space (CSS) method and the modified Fourier descriptor (MFD) method. In addition, the twostage approach can achieve a performance comparable to an exhaustive search, but with a much reduced computational complexity.1 IntroductionPlant identification is a process resulting in the assignment of each individual plant to a descending series of related plant groups in ternis of their common characteristics.Such a task is very demanding in biology and agriculture, such as for the discovery of new species, plant resource surveys and plant species database management. So far, this time-consuming process has mainly been carried out by botanists. A significant improvement can be expected if the plant identification can be carried out by a computer, automatically or semautomatically,assisted by image processing and computer vision techniques. By using a computer-aided plant identification system, non-professionals can also identify many plant species. This will promote an interest in studying plant taxonomy and ecology, and lift primary and secondary biology education standards at various levels, and promote the use of infonnation technology for modernising the managcmcnt of botanical gardens,natural reserve parks and forest plantation.A computer plays three major roles in computer-aided plant identification: information storage and retrieval,automatic image and text information processing, and the use of machine learning techniques for deriving decision trees for plant identification. Fig. 1 shows the simplified block diagram that we proposed for a computer-aided living plant identification system. In identifying plant species, huinan beings will observe one or more of the following: the whole plant (form, size etc.), leaves (organisation,shape, margin, venation patterns etc.), flowers(inflorescence, growing position, colour, size, symmetry etc.), stem (shape, nodc, bark patterns, outer character etc.), fruits (size: colour, outer character, quality etc.).Some of these human observations can be carried out bya computer when the corresponding images are provided.However, automatic (machine) plant recognition from colour images is still one of the most difficult tasks in computer vision, duc to: (i) lack of proper models or representations, (ii) a great number of biological variations that a species of plants can take, and (iii) imprecise image prc-processing techniques such as edge detection andcontour extraction, thus resulting in possible missing features. Owing to many difficulties involved, research and development in computer-aided identification is still in its infancy.Plant leaves have an approximately two-dimensional nature an4 therefore, they are most suitable for machine processing. As the shape of plant leaves is one of the most important features for characterising various plants visually, the study of leaf image retrieval schemes will be an important stage for developing a plant identification system. By using such a leaf retrieval subsystem, a user can have a number of top-matched leaves together with their whole plant images and text descriptions displayed on the screen, which will help the user to further refine his/her identification by doing more observations.Im ef a/. [ I ] and Abbasi ef a/. [2] have done some preliminary work on plant recognition and classification with the shape features of plant leaves. Im ef a/. used a hierarchical polygon approximation representation of leaf shape to recognize Acer tree family varieties. A curvature scale space (CSS) image was used to'iepresent leaf shapes for chrysanthemum variety classification by Abbasi et a/. [2]. They represented each object with the maxima of its curvature zero-crossing CSS image contours and the similarity between two different shapes was expressed with a real value by matching their maxima sequences. In most situations, a user may even not know what species a plant is and he/she expects the computer to provide more information for further investigation and decision-making.Shape is one of the most important features for characterizing an object and is commonly used in object recognition,matching and registration. Many investigations on shape representation [3-5], such as chain codes, centroid-contour distance (CCD) curve, medial axis transform (MAT) [6],Fourier descriptors (FDs) [7-91, wavelet descriptors [IO],moment invariants (MI) [ I I ] and deformable templates[12, 4], have been carricd out. All of them perform well for some specific applications with their advantages. There alsn have been some succcssiid applications reportcd in the literature [IZ-14]. An important and essential criterion for shapc representation is that the representation is invariant to translation, scaling and rotation of images or objects. Therefore,much research has also been conducted further on the above basic algorithms to achieve better performance and efficiency. In [7-91. the authors achieved translation, scaling and rotation invariance ofFDs by normalisation and matching methods. These FDs are computationally efficient on either FD extraction or FD matching. Rui er al. [I5] proposed a modified Fourier descriptor (MFD) to achieve translation, scaling and rotation invariance, by considering the distances of FD magnitude and phase angle separately.and to decrease the discrctisation noise. Considering its advantages and computational efficiency, we have chosen it as one of the test methods.In this paper, an application of leaf image retrieval based on three simple sets of shape features is presented. First,we introduce a leaf shape representation with the centrnidcontour distance (CCD) curve. There are two main variations for the idea of the CCD curve: one is that the contourpoints are selected so that the central angles between twn sequential contour points are equal, the other is proposed by Chang et ul. [16]in which high-curvature feature points are selected. To preserve contour information, we make use of all the sample points along contours by following the basic CCD curve idca. It will he demonstrated that this translation-invariant representation can achieve scaling invariancc by normalisation and the rotation invariancc with CCD similarity measure can be achieved by aligning starting points. Considering the exhaustive aligning whichis time-consuming, a thinning-based starting-point location method is proposed to reduce the matching time. Actually,such a method will benefit other shape representations which are sensitive to starting points. On the other hand,an angle code histogram (ACH) is adopted to characterize local fcatures of the leaf shape, because the CCD curvc is poor in characterising the local details after being normalised.After studying the ways for leaf classification by botanists,we propose using a two-stage approach which makes use of the eccentricity measure only in the first stage so as to further reducethe rctrieval time, ACH and the CCD curve are combined with the eccentricity feature in the second stage of fine retricval.2 Centroid-contour distance (CCD) curveTracing a leaf contour can be considered as circling around its centroid. The tracing path along one direction (clockwise or anticlockwise) from a fixed starting point represents a shape contour uniquely, i.e. a contour point sequence corresponds to a shape uniquely ifthe starting point is fixed.This is actually the basic idea for the chain code representation of a shape.作者：Z. Wang, Z. Chi and D. Feng国籍：中国出处：/xpl/articleDetails.jsp?tp=&arnumber=1192289&queryText%3DShape+based +leaf+image+retrieval基于形状的叶片图像检索摘要：作者呈现了一种二阶段的高效方式实现叶片图像检索，这种方式使用轮廓质心距离曲线（CCD）、偏心率和倾角码直方图（ACH）这些简单的图形特征来完成。

多类运动想象脑电信号特征提取与分类

多类运动想象脑电信号特征提取与分类
段锁林，尚允坤，潘礼正
（常州大学机器人研究所，江苏常州２１３１６４）
摘要：针对多类运动想象情况下存在的脑电信号识别正确率比较低的问题，提出了一种基于小波包特定频段的小波包方差，小波包熵和共同空间模式相结合的脑电信号特征提取的方法，并将特征向量输入到支持向量机中达到分类的目的；首先选择重要导联的脑电信号，进行特定频段的小波包去噪和分解；其次对通道优化的重要导联的每个通道信号计算小波包方差和小波包熵值作为特征向量；然后对所有重要导联的分解系数重构并进行共同空间模式特征提取；最后结合２种不同导联方式所获取的特征向量作为分类器的输入进行分类；采用ＢＣＩ２００５ｄｅｓｃ—Ｉｌｉａ中１１ｂ数据进行验证，该算法的分类正确率最高达到８８．７５，相对２种单一的提取方法分别提高２８．２７和６．５５；结果表明该算法能够有效提取特征向量，进而改善多类识别正确率较低的问题。
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稳态视觉诱发电位和运动想象脑电特征分析及混合bci研究

中文摘要摘要脑机接口（Brain-computer Interface, BCI）是一种能在大脑与计算机或其他电子设备间建立直接通讯和控制通道的交互系统，在神经工程、康复和脑科学领域研究及应用越来越广泛。

根据获取脑电信号方式的差异，BCI分为有创和无创。

其中基于稳态视觉诱发电位（steady-state visual evoked potential, SSVEP）和运动想象的无创BCI是目前研究和应用最广泛的BCI系统。

虽然低频刺激模式下的SSVEP特征明显、易于检测。

但低频刺激容易使受试者产生疲劳，甚至对某些受试者存在诱发光敏癫痫疾病的风险。

因此，为提高舒适性和降低风险，采用高频刺激模式下的SSVEP是BCI研究的发展方向。

然而，高频SSVEP特征不易检测，因此，如何在提高舒适度同时，保证特征分类效果，是基于SSVEP的BCI系统急需解决的问题。

另一方面，当受试者想象肢体运动时，在大脑感觉运动皮层区域，会出现事件相关同步和去同步现象（event-related desynchronization/synchronization,ERD/ERS），这是运动想象EEG信号的主要特征。

现有文献认为该现象主要反映在EEG信号幅值特征上，因此，对其在相位耦合特征上的表现，尤其是零相位耦合特征的意义，以及幅值和相位耦合特征之间的关系等，尚缺乏深入研究。

目前运动想象BCI还存在训练时间长，涉及导联数较多等缺点。

因此，如何选择导联，以及在少导联情况下，如何保证特征任务预测能力，对运动想象BCI研究具有重要意义。

为充分发挥高频刺激SSVEP和运动想象EEG的优点，克服低频刺激易疲劳和运动想象BCI盲现象等问题，通过对SSVEP和运动想象两种EEG信号特征的深入研究，本文提出少导联情况下，基于高频刺激SSVEP和运动想象EEG的新型混合BCI。

此方法能有效降低单一模态BCI盲现象，并提高分类准确率。

本文的主要内容包括以下几个方面：①为提高SSVEP-BCI的舒适度，本文对比研究了低频和中高频SSVEP信号特征，提出了适合中高频SSVEP的最佳参考导联选择机制和特征提取方法，并将该方法应用于混合BCI的高频SSVEP特征分析中，从而在少导联情况下，有效提取高频SSVEP特征。

fx 使用手册

1.3 练的合并分割的方法叫 thresholding，这种方法有利于提取点的特征 (例如：飞机)。Thresholding 是一个可选项，对 Region Means 影像第一波段进行处理，合并临近分割。对于和背景具有高对比度的地物 Thresholding 提取的地物效果非常好 (例如,白色的船与深色的水)。选择以下选项的其中一项： No Thresholding (默认)：跳过这一步，进行下一步操作； Thresholding (advanced) ：如果选择该选项，会弹出 Region Means 影像的直方图, 按照以下过程继续操作：（1）点击预览 Preview，出现预览窗口；（2）点击并拖拉直方土图上白色虚线来确定最大最小域值，预览窗口只根据 DN 值将影像分割成白色和黑色，白色表示前景，黑色表示背景，如果不改变虚线表示 No Thresholding；注意：可以通过调整透明度来预览影像分割效果。如图 2-4 所示
1.4 计算属性（Computing Attributes）
在这一步里， ENVI Zoom 为每一个目标物计算 spatial, spectral, 以及 texture 等属性。如图 2-5 所示。
—————————————————————————————————————————————— 010-62054260/1/2/3 北京市朝阳区德胜门外华严北里甲 1 号健翔山庄 D5-D6
航天星图科技（北京）有限公司
FX 特征提取模块使用手册
一、ENVI FX 特征提取模块介绍
ENVI 特征提取模块（ENVI Feature Extraction）基于影像空间以及影像光谱特征，从高分辨率全色或者多光谱数据中提取信息，该模块可以提取各种特征地物如车辆、建筑、道路、河流、桥、河流、湖泊以及田地等。令人欣慰的是该模块可以预览影像分割效果，还有就是它基于目标来对影像进行分类（传统的影像分类是基于像素的，也就是说利用每个像素的光谱信息对影像进行分类）。该项技术对于高光谱数据有很好的处理效果，对全色数据一样适用。对于高分辨率全色数据，这种基于目标的提取方法能更好的提取各种具有特征类型的地物。一个目标物体是一个关于大小、光谱以及纹理（亮度、颜色等）的感兴趣区域, ENVI FX 能同时定义多个这样的感兴趣区域。 ENVI Feature Extraction 将影像分割成不同的区域, 产生目标区域，这个流程很有帮助而且直观 , 同时允许您定制自己的应用程序。整个流程如下图所示:

3 Feature Extraction I

Fall 2004
Pattern Recognition for Vision
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Fall 2004 Pattern Recognition for Vision

基于运动想象的脑电特征提取及特征迁移方法研究

摘要运动想象脑-机接口技术不依赖人的外周神经和肌肉组织，直接实现人脑对外部设备的控制，它可以帮助有运动障碍的患者，更好地与外界进行信息交流，在军事、航天、医疗和虚拟现实等领域有巨大的应用价值。

脑电信号具有非平稳性，而传统运动想象技术在应用前需要标注大量的训练样本，并采用多通道采集的方式，这大大限制了其应用范围。

本文在传统脑电信号处理方法的基础上，将迁移学习的思想应用于运动想象的分类，减少训练样本和测试样本的分布差异，以提高分类准确率。

此外，针对运动想象技术对运算实时性要求高的问题，研究通道选择优化方法，在保证分类正确率损失有限的条件下，减少分析脑电信号的通道数量，以提高运动想象脑-机接口技术的实时性。

本文具体研究工作如下。

基于运动想象生理基础，研究运动想象脑电信号预处理方法。

利用AR模型对运动想象脑电信号频谱分析，得出信号有效的频带范围8-30Hz，为滤波器通带频率的选择提供分析依据；并分析公共平均参考法（CAR）空间滤波增加不同思维脑电信号空间分布差异的优势，为获得高信噪比的脑电信号奠定基础。

研究基于小波包变换的特征提取方法，选择小波包分解后特定子节点的小波系数，并提取能量特征，利用支持向量机，识别两种类型的运动想象任务，得出平均分类正确率为79.4%。

在此基础上，研究通道选择的优化方法，基于Relief-F 算法计算通道权重，在对分类效果影响有限的条件下，减少分析脑电信号的通道数量，有助于减少计算量，提高运动想象脑-机接口实时性。

研究基于最小化MMD的迁移学习算法，并将算法应用于运动想象的分类。

结果表明，该方法有助于提高实验者一段时间内运动想象的分类正确率，且能够使一个实验者训练的分类模型更加适用于另一个实验者的测试。

证明了迁移学习算法比传统的分类方法有更好的适应性。

结合以上研究，设计基于运动想象迁移学习实验。

针对真实的脑电信号含有的伪迹问题，研究小波分析眼电伪迹滤除的方法，并探讨迁移学习在线实现方案。

基于运动想象脑机接口的AuFBCSP方法

基于运动想象脑机接口的AuFBCSP方法侯秉文;刘鹏;周广玉;何嘉全【摘要】This paper proposes the augmented Filter Bank Common Spatial Pattern, a novel feature extraction al- gorithm for the motor imagery-based brain-computer interface. Compared with the Filter Bank Common Spatial Pattern, our method takes into full consideration the specificity temporal information, in which ERD happens in motor imagery. In this way, more features are acquired, and the higher accuracy and kappa value are obtained by these features. Two kinds of feature selection algorithms are employed in the BCI competition dataset. The results show that the accuracy based method yields superior classification accuracy compared with that based on mutual information.%针对基于运动想象的脑机接口问题，提出了一种新的特征提取方法，即加强的滤波带宽共同空间模式方法。

与传统的滤波带宽共同空间模式方法相比，文中提出的方法充分考虑了被试在进行运动想象时发生事件相关去同步时段的特异性，从而得到更多的特征，并利用这些特征分类，取得更高的准确率和kappa值。

关于脑电信号提取的文献综述

脑电信号特征提取及分类文献综述胡雪寅 3080104819一、引言脑电信号是脑神经细胞的电生理活动在大脑皮层或头皮表面的总体反映。

而脑机接口（Brain -computer interface，BCI）是建立在脑电信号分析基础上的一种生物技术和计算机技术相结合的应用型研究，它提供了一种新型的人机交互方式，通过制定的脑机接口系统，利用相应的外部设备，直接产生人脑所想象的相应动作。

脑机接口系统的核心是对脑电信号的提取与分析，特别是相应的想象所产生的脑电信号特征提取。

通过思维活动与脑电信号的对比，可以形成脑电信号-思维活动的对应关系。

脑机接口以及脑电信号特征的提取与分类既是人类了解和提高脑功能的重要手段,又是一种全新的通讯和控制方式,在脑科学、康复工程、生物医学工程、娱乐、外科手术中功能区定位等领域有广泛的应用前景。

二、脑电信号特征的提取与分类的方法对于不同的脑电信号所使用的特征提取与分类方法是不相同的。

常用的特征提取方法有FFT、相关性分析、AR参数估计、CSP、Butterworth低通滤波、遗传算法、小波变换等，算法的选择与所利用的信号特征及电极位置有关。

但目前主流的 EEG 信号特征抽取方法有：一种是传统时频特征组合法，将时域均值、频域功率谱组合作为特征矢量，主要是利用多种类别信息提供更多的特征，但较多的特征使得建立分类器的模型比较复杂，不利于实际系统中的应用；另一种是小波变化系数法，依据先验知识、抽取感兴趣频段的小波系数作为特征，但选择不同的小波对分类结果有一定的影响。

而分类方法主要有决策树、局部BP 算法、贝叶斯分类器、MLP、支持向量机（SVM）等。

以下简要介绍各种脑电特征提取与分类的方法。

（1）基于能量特征的脑电信号特征提取与分类：该方法采用带通滤波和小波包分析的方法提取Mu、Beta节律对应的脑电信号,在时域范围内,将信号幅度的平方作为能量特征值;在频域范围内,采用AR模型功率谱估计法所得的功率谱密度作为能量特征值。

Feature Extraction And Modeling Of Urban Building From Vehicle-Borne Laser Scanning Data

1
high-quality DEM directly [Weidner 1996, Brunn 1997]. Because the range image consists of discrete points, without topological relation, boundary attribute and object feature, it brings about the uncertainty of extracted object. The key to the problem focuses on how to distinguish different objects and construct model respectively. Manandhar (2001) has classified scanning points according to the spatial distribution feature of laser points in each scan line. Although the data processing is complex, it can separate buildings, roads and trees etc. Li (2003) researched linear building feature extraction from range images. But all those are limited in extracting only one lateral feature of buildings. Because hitherto there are no matured feasible methods of segmentation and feature extraction from range image, current laser-scanning systems are all integrated with CCD or similar image acquisition devices. The range images are mainly used as a supplement of photogrammetry or be constructed high-quality DEM/DSM, and the CCD image data for image segmentation and feature extraction [Ackermann 1999]. This collaborative mode has the character with the high cost of time, the large quantities of data-storage and the complex processing and integration of multi-source data. Segmentation is the base of identification, location and modeling of objects. This paper mainly researches the technology and method of segmentation and extraction from range images captured by vehicle-borne laser scanning system.

基于深度卷积网络的运动想象脑电信号分类方法

基于深度卷积网络的运动想象脑电信号分类方法ClassificaPi os MePhod of MoPor Imagery EEG Sig sals Based os Deep Cos v oluPi onal NePwork 赵龙辉李力陈奕辉林诗柔（广东工业大学自动化学院，广东广州510006）摘要：为了提高多分类运动想象脑电信号的解码精度,以此促进脑机接口系统在生产生活中的应用。

采用基于深度卷积网络的LaNat和AlaxNat模型分析四分类运动想象脑电特性。

将脑电信号通过预处理、数据归一化和数据增强，然后分别输入两个模型中进行分类遥通过与现有不同的特征提取和分类方法对比,实验结果表明,在多分类运动想象脑电解码研究领域中，深度卷积网络模型取得的分类效果较好。

关键词:脑机接口；运动想象；深度卷积网络;脑电分类Abstract:In order to occurately extract pnd classify the EEG features of motor imagination,so os to promote th^application of the brain-computer interface system in production and life.The LeNet pnd AlexNet models based on deep convolutional networks sre used to onalyze the four-category motor imagery EEG characteristics in this paper.The EEG signals sre preprocessed,data normalized and data enhanced,and then input into two models for classification.By comparing with the existing different feature extraction and classification methods,the experimental results show that the deep convolutional network model lchieves better classification results in the field of multi-class motor imaging EEG decoding research.Keywords:brain-computer interface,motor imagination,deep convolutional network,EEG classification脑-(计算机)机接口(Brain-computar Intarfaca,BCI)可以简单定义为提供大脑控制外部设备以实现通信或控制的一个系统[1]O BCI有显著的潜力可以帮人们替代或恢复由疾病或伤害而受损的功能。

多通道三维视觉指导运动想象脑电信号特征选择算法

收稿日期：２０１８０８２８；修回日期：２０１８１０１５基金项目：重庆市科委基础与前沿研究计划项目（ｃｓｔｃ２０１４ｊｃｙｊＡ４００３９）作者简介：胡敏（１９７１），女，重庆人，副教授，硕士，主要研究方向为虚拟现实、脑机接口、通信网体系协议；王志强（１９９０），男，山西吕梁人，硕士，主要研究方向为脑机接口、虚拟现实交互、全景视频；黄宏程（１９７９），男（通信作者），河南南阳人，副教授，博士，主要研究方向为模式识别、数据融合通信（ｈｕａｎｇｈｃ＠ｃｑｕｐｔ．ｅｄｕ．ｃｎ）；李冲（１９９２），男，安徽阜阳人，硕士，主要研究方向为脑机接口、虚拟现实交互．多通道三维视觉指导运动想象脑电信号特征选择算法胡　敏，王志强，黄宏程，李　冲（重庆邮电大学通信与信息工程学院，重庆４０００６５）摘　要：针对基于三维视觉指导的运动想象脑机接口多通道冗余信息较多、分类准确率差的问题，提出了一种基于小波包分解（ＷＰＤ）—共空间滤波（ＣＳＰ）—自适应差分进化（ＡＤＥ）的模式脑电信号特征提取与选择分类方法。

首先，对采集的多通道运动想象脑电信号进行ＷＰＤ变化，划分出精细的子频带；然后，分别将ＷＰＤ变换后的每个子空间作为ＣＳＰ的输入，得到对应的特征向量；最后，使用ＡＤＥ算法对特征向量进行选择，选择出用于分类的最佳特征子集。

采用ＷＰＤＣＳＰＡＤＥ模式进行特征提取与选择，较经典的ＷＰＤＣＳＰ方法在分类正确率、特征个数方面有着更好的表现。

同时，所提算法分类性能明显优于遗传算法、粒子群算法。

实验结果表明，ＷＰＤＣＳＰＡＤＥ方法能够有效地提高分类正确率，同时减少了用于分类的特征个数。

关键词：脑机接口；运动想象；脑电信号；特征选择；自适应差分进化中图分类号：ＴＮ９１１．７文献标志码：Ａ文章编号：１００１３６９５（２０２０）０３０３３０７９４０５ｄｏｉ：１０．１９７３４／ｊ．ｉｓｓｎ．１００１３６９５．２０１８．０８．０６３０Ｆｅａｔｕｒｅｓｅｌｅｃｔｉｏｎａｌｇｏｒｉｔｈｍｆｏｒ３ＤｖｉｓｕａｌｇｕｉｄａｎｃｅｍｕｌｔｉｃｈａｎｎｅｌｍｏｔｏｒｉｍａｇｅｒｙＨｕＭｉｎ，ＷａｎｇＺｈｉｑｉａｎｇ，ＨｕａｎｇＨｏｎｇｃｈｅｎｇ，ＬｉＣｈｏｎｇ（ＳｃｈｏｏｌｏｆＣｏｍｍｕｎｉｃａｔｉｏｎ＆ＩｎｆｏｒｍａｔｉｏｎＥｎｇｉｎｅｅｒｉｎｇ，ＣｈｏｎｇｑｉｎｇＵｎｉｖｅｒｓｉｔｙｏｆＰｏｓｔｓ＆Ｔｅｌｅｃｏｍｍｕｎｉｃａｔｉｏｎｓ，Ｃｈｏｎｇｑｉｎｇ４０００６５，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：ＣｏｎｃｅｒｎｉｎｇｔｈｅｐｒｏｂｌｅｍｔｈａｔｍｕｌｔｉｃｈａｎｎｅｌｍｏｔｏｒｉｍａｇｅｒｙｏｆＢＣＩｂａｓｅｄｏｎ３Ｄｖｉｓｕａｌｇｕｉｄａｎｃｅｗｉｔｈｍｏｒｅｒｅｄｕｎｄａｎｃｙｉｎｆｏｒｍａｔｉｏｎａｎｄｐｏｏｒｃｌａｓｓｉｆｉｃａｔｉｏｎａｃｃｕｒａｃｙ，ｔｈｉｓｐａｐｅｒｐｒｏｐｏｓｅｄａｐａｔｔｅｒｎｃｌａｓｓｉｆｉｃａｔｉｏｎｍｅｔｈｏｄｂａｓｅｄｏｎＷＰＤＣＳＰＡＤＥｆｏｒｆｅａｔｕｒｅｅｘｔｒａｃｔｉｏｎｏｆＥＥＧ．Ｆｉｒｓｔｌｙ，ｔｈｉｓａｌｇｏｒｉｔｈｍｕｓｅｄＷＰＤｔｏｄｉｖｉｄｅｔｈｅｍｕｌｔｉｃｈａｎｎｅｌｍｏｔｉｏｎｉｍａｇｅｒｙＥＥＧｓｉｇｎａｌｓｉｎｔｏｆｉｎｅｓｕｂｂａｎｄｓ．Ｓｅｃｏｎｄｌｙ，ｉｔｕｓｅｄＣＳＰｔｏｏｂｔａｉｎｔｈｅｅｉｇｅｎｖｅｃｔｏｒｓｃｏｒｒｅｓｐｏｎｄｉｎｇｔｏｅａｃｈｓｕｂｓｐａｃｅｏｆＷＰＤｔｒａｎｓｆｏｒｍａｔｉｏｎ．Ｆｉｎａｌｌｙ，ｉｔｓｅｌｅｃｔｅｄｔｈｅｆｅａｔｕｒｅｖｅｃｔｏｒｓｔｈｒｏｕｇｈｔｈｅＡＤＥａｌｇｏｒｉｔｈｍｔｏｏｂｔａｉｎｔｈｅｂｅｓｔｆｅａｔｕｒｅｓｕｂｓｅｔｓｆｏｒｃｌａｓｓｉｆｉｃａｔｉｏｎ．ＵｓｉｎｇＷＰＤＣＳＰＡＤＥｍｏｄｅｆｏｒｆｅａｔｕｒｅｅｘｔｒａｃｔｉｏｎａｎｄｓｅｌｅｃｔｉｏｎ，ｉｔｈａｓｂｅｔｔｅｒｐｅｒｆｏｒｍａｎｃｅｉｎｃｌａｓｓｉｆｉｃａｔｉｏｎａｃｃｕｒａｃｙａｎｄｎｕｍｂｅｒｏｆｆｅａｔｕｒｅｓｔｈａｎｔｈｅｃｌａｓｓｉｃＷＰＤＣＳＰｍｅｔｈｏｄ．Ａｔｔｈｅｓａｍｅｔｉｍｅ，ｔｈｅｃｌａｓｓｉｆｉｃａｔｉｏｎｐｅｒｆｏｒｍａｎｃｅｏｆｔｈｅｐｒｏｐｏｓｅｄａｌｇｏｒｉｔｈｍｉｓｓｉｇｎｉｆｉｃａｎｔｌｙｂｅｔｔｅｒｔｈａｎｔｈｅｇｅｎｅｔｉｃａｌｇｏｒｉｔｈｍａｎｄｐａｒｔｉｃｌｅｓｗａｒｍｏｐｔｉｍｉｚａｔｉｏｎａｌｇｏｒｉｔｈｍ．ＴｈｅｅｘｐｅｒｉｍｅｎｔｓｓｈｏｗｔｈａｔｔｈｅＷＰＤＣＳＰＡＤＥｍｅｔｈｏｄｃａｎｅｆｆｅｃｔｉｖｅｌｙｉｍｐｒｏｖｅｔｈｅｃｌａｓｓｉｆｉｃａｔｉｏｎａｃｃｕｒａｃｙａｎｄｒｅｄｕｃｅｔｈｅｎｕｍｂｅｒｏｆｆｅａｔｕｒｅｓｕｓｅｄｆｏｒｃｌａｓｓｉｆｉｃａｔｉｏｎ．Ｋｅｙｗｏｒｄｓ：ｂｒａｉｎｃｏｍｐｕｔｅｒｉｎｔｅｒｆａｃｅ（ＢＣＩ）；ｍｏｔｏｒｉｍａｇｅｒｙ（ＭＩ）；ｅｌｅｃｔｒｏｅｎｃｅｐｈａｌｏｇｒａｍ（ＥＥＧ）；ｆｅａｔｕｒｅｓｅｌｅｃｔｉｏｎ；ａｄａｐｔｉｖｅｄｉｆｆｅｒｅｎｔｉａｌｅｖｏｌｕｔｉｏｎ（ＡＤＥ）脑机接口（ＢＣＩ）作为一种新兴的人机交互方式，在大脑和计算机等外部电子设备之间建立了不依赖于外周神经和肌肉组织的信息传输通道［１，２］。

FEATURE EXTRACTION DEVICE, FEATURE EXTRACTION METH

专利内容由知识产权出版社提供
专利名称：FEATURE EXTRACTION DEVIAM FOR SAME
发明人：YAGI, TAKESHI,KITSUKAWA, TAKASHI 申请号：EP 11834 034 申请日：20111018 公开号：EP2631872A4 公开日：20151028
申请人：OSAKA UNIVERSITY
更多信息请下载全文后查看
摘要：Provided is a feature extraction device that is capable of extracting the features of a recognition subject without prior training. The feature extraction device (100) according to the present invention comprises a neural network provided with a plurality of neurons (301, 302, 303, 311, 312, 313) constituting a calculation unit. The plurality of neurons each have one or more expressed gene constituting attribute values for determining whether a signal can be transmitted from one set of neurons (301, 302, 303) to the other set of neurons (311, 312, 313). Input data obtained by dividing target data to be subjected to feature extraction is input to a first neuron from among the plurality of neurons, and the first neuron outputs a first signal value, which is a value that increases as the value of the input data increases, to a second neuron having the same expressed gene as the expressed gene of the first neuron. The second neuron calculates, as the feature quantity of the target data, a second signal value which is a value corresponding to the sum of the first signal values that have been input.

基于运动想象的脑电信号特征提取研究

基于运动想象的脑电信号特征提取研究郭闽榕(福州大学数学与计算机科学学院，福建福州350000)摘要：基于运动想象脑电信号的脑-机接口系统在医疗领域具有广阔的应用前景，被应用于运动障碍人士的辅助控制以及脑卒的预后康复$由于运动想象的脑电信号信噪比低、不平稳以及差异性显著，对脑电信号识别带来负面影响$—个有效的特征提取算法能够提高脑-机系统的脑电信号识别率$提出一种多通道的脑电信号特征提取方法，将数据矩阵分解为基矩阵与系数矩阵的乘积，以类间离散度做为性能判据对系数矩阵进行特征提取，提取可分性更高、维数更少的特征$结合脑电信号识别领域常见的分类器在2008年BCI竞赛数据集上进行验证，证明所提方法是有效的$关键词：脑机接口；脑电信号；运动想象；特征提取；矩阵分解中图分类号：TP391.4文献标识码：I DOI:10.19358/j.issn.2096-5133.2021.01.011引用格式：郭闽榕.基于运动想象的脑电信号特征提取研究[J].信息技术与网络安全,2021,40(1):62-66.Feature extraction of EEG signals based on motor imageryGuo Minrong(College of Mathematics and Computer Science,Fuzhou University,Fuzhou350000,China)Abstract:The brain-computer interface(BCI)system based on motor imagery(MI)electroencephalogram(EEG)has a broad application prospect in the medical field,which can be applied to the auxiliary control of the disabled and the prognosis and rehabilitation of the brain.Because of the low SNR,instability and significant difference of EEG signal in motion imagination,it has a negative effect on EEG signal recognition.An effective feature extraction method can enhance the accuracy of EEG in BCI system.In this paper,a multi一channel feature extraction method for EEG signals is proposed. First of all,the data matrix is decomposed into the product of the basis matrix and the coefficient matrix.Then the coefficient matrix is extracted by using the inter-class dispersion as the performance criterion to extract the features with higher separability and less dimension.The experiment of BCI2008competition data set shows that the method is effective. Key words:brain-computer interface；electroencephalogram；motor imagery；feature extraction；matrix decomposition0引言脑-机接口[1](Brain-Computer Interface,BCI)系统是一种不需要任何外部肌肉活动的通信系统，能够将大脑活动产生的脑信号转化为对电子设备的指令。

基于通道选择的多尺度Inception网络的脑电信号分类研究

现代电子技术Modern Electronics Technique2023年12月1日第46卷第23期Dec. 2023Vol. 46 No. 230 引言脑机接口（Brain⁃Computer Interface, BCI ）是一种通过分析神经元电信号，促进人脑与外部电子设备直接通信的技术[1]。

BCI 系统最初是为帮助患有身体或认知障碍的患者而开发的，现已在神经医学、智能家居、自动驾驶和娱乐等领域得到广泛应用[2]。

BCI 系统包括收集大脑信号、对其进行解码和控制外部设备（例如计算机、智能轮椅或假肢）三部分组件[3]。

记录人脑活动意图的技术分为侵入式和非侵入式两种。

侵入性技术需要植入微电极阵列，存在一定的风险[4]；非侵入性技术如脑电图（Electroencephalography, EEG ）是主要采用的研究方基于通道选择的多尺度Inception 网络的脑电信号分类研究刘培，宋耀莲（昆明理工大学信息工程与自动化学院，云南昆明 650500）摘要：基于运动想象脑电信号的脑机接口系统有可能在大脑和外部设备之间创建通信通道。

然而，特征提取的局限性、通道选择的复杂性和被试者之间的可变性使得脑电信号分类模型难以有效泛化。

在这项研究中，文中提出一种端到端的深度学习模型，该模型使用并行多尺度Inception 卷积神经网络在6个通道选择区域中进行多分类运动想象任务。

为了解决被试者间可变性，实验进行了跨被试和跨被试微调两种评估场景。

在BCI 竞赛IV 2a 数据集上的实验和测试结果表明：ROI F 达到了98.49%的最高分类精度，比最低准确率高17.26%；且跨被试微调场景分类性能优于被试内和跨被试场景，分类准确率分别提高了1.82%和1.69%。

此外，并行多尺度Inception 卷积神经网络模型的平均分类准确率比单尺度Inception CNN 模型高5.17%。

总之，文中提出一种基于通道选择的端到端的脑电信号分类框架，可以促进高性能和稳健的脑机接口系统的开发。

基于Mu_Beta节律想象运动脑电信号特征的提取

中国组织工程研究与临床康复第 14 卷第 43 期 2010–10–22 出版October 22, 2010 Vol.14, No.43Journal of Clinical Rehabilitative Tissue Engineering Research基于Mu/Beta节律想象运动脑电信号特征的提取*★黄思娟，吴效明Feature extraction of electroencephalogram for imagery movement based on Mu/Beta rhythmHuang Si-juan, Wu Xiao-mingAbstractBACKGROUND: Different sports produce different electroencephalogram (EEG) signals. Brain-computer interface (BCI) utilized characteristics of EEG to communicate brain and external device by modern signal processing technique and external connections. The speed of EEG signals processing is important for BCI online research. OBJECTIVE: To investigate a rapid and accurate method for extracting and classifying EEG for imagery movement. METHODS: Using the attribute of event-related synchronization and event-related desynchronization during imagery movement, the BCI dataset of 2003 was processed. Mu/Beta rhythm was obtained from bandpass filtering and wavelet package analysis. Then feature was formed by the average energy of lead C3, C4, and was sorted out by the function classify of matlab. RESULTS AND CONCLUSION: Appropriate parameters were obtained by detection of training data and used for identification of training data and testing data, with a correct rate of classification of 87.857% and 88.571%. Huang SJ, Wu XM. Feature extraction of electroencephalogram for imagery movement based on Mu/Beta rhythm.Zhongguo Zuzhi Gongcheng Yanjiu yu Linchuang Kangfu. 2010;14(43): 8061-8064. [ ]School of Bioscience and Bioengineer, South China University of Technology, Guangzhou 510006, Guangdong Province, China Huang Si-juan★, Studying for master’s degree, School of Bioscience and Bioengineer, South China University of Technology, Guangzhou 510006, Guangdong Province, China huangsijuan123@ Correspondence to: Wu Xiao-ming, Doctoral supervisor, School of Bioscience and Bioengineer, South China University of Technology, Guangzhou 510006, Guangdong Province, China bmxmwus@scut. Supported by: the Science and Technology Development Program of Guangdong Province, No. 2009B030801004* Received: 2010-05-17 Accepted: 2010-07-13摘要背景：不同的运动会产生不同的脑电信号，脑机接口技术就是利用脑电信号的特异性，通过现代信号处理技术和外部的连接实现人脑与外部设备的通信。