分享
2022年医学专题—EEG-signal-processing-脑电信号处理方法算法.ppt
下载文档

ID:2519579

大小:5.37MB

页数:27页

格式:PPT

时间:2023-06-29

收藏 分享赚钱
温馨提示:
1. 部分包含数学公式或PPT动画的文件,查看预览时可能会显示错乱或异常,文件下载后无此问题,请放心下载。
2. 本文档由用户上传,版权归属用户,汇文网负责整理代发布。如果您对本文档版权有争议请及时联系客服。
3. 下载前请仔细阅读文档内容,确认文档内容符合您的需求后进行下载,若出现内容与标题不符可向本站投诉处理。
4. 下载文档时可能由于网络波动等原因无法下载或下载错误,付费完成后未能成功下载的用户请联系客服处理。
网站客服:3074922707
2022 医学 专题 EEG signal processing 电信号 处理 方法 算法
EEG SIGNAL PROCESSING,第一页,共二十七页。,EEG signal modelling,1,Available features,2,Classification algorithms,3,Independent Component Analysis,4,Content,Sparse Representation,5,第二页,共二十七页。,1,EEG signal modelling,Bioelectricity,1,Signal generation system,2,第三页,共二十七页。,BIOELECTRICITY,SIGNAL GENERATION SYSTEM,Excitation model,第四页,共二十七页。,SIGNAL GENERATION SYSTEM,BIOELECTRICITY,Linear Model,第五页,共二十七页。,SIGNAL GENERATION SYSTEM,BIOELECTRICITY,Nonlinear Model,第六页,共二十七页。,2,Available features,Basic features,1,Modern methods,2,第七页,共二十七页。,Temporal Analysis Signal Segmentation:label the EEG signals by segments of similar characteristics.,BASIC FEATURES,MODERN METHODS,第八页,共二十七页。,Temporal Criteria,BASIC FEATURES,MODERN METHODS,第九页,共二十七页。,Frequency AnalysisSuboptimal DFT,DCT,DWT;Optimal KLT(Karhunen-Love)Demerits:complete statistical information,no fast calculation.,BASIC FEATURES,MODERN METHODS,第十页,共二十七页。,Signal Parameter Estimation AR model:Merits:Outperform DFT in frequency accuracy.Demerits:suffer from poor estimation of parameters.Improvements:accurate order&coefficients.,MODERN METHODS,BASIC FEATURES,第十一页,共二十七页。,AR coefficients estimation methodsYule-Walker aryule(x,p)Merits:Toeplitz matrix Levinson-Durbin,fastest!Demerits:with window bad resolution of PSD,MODERN METHODS,BASIC FEATURES,第十二页,共二十七页。,AR coefficients estimation methodsCovariance method arcov(x,p),armcov(x,p)Merits:without window good resolution of PSD Demerits:slowBurg arburg(x,p)Merits:accurate approximation of PSD Demerits:line skewing&splitting,MODERN METHODS,BASIC FEATURES,第十三页,共二十七页。,MODERN METHODS,BASIC FEATURES,Comparison,第十四页,共二十七页。,Principal Component AnalysisUse same concept as SVDDecompose data into uncorrelated orthogonal componentsAutocorrelation matrix is diagonalizedEach eigenvector represents a principal componentApplication decomposition,classification,filtering,denoising,whitening.,MODERN METHODS,BASIC FEATURES,第十五页,共二十七页。,3,Sparse Representation,Sparse Approximation,1,Sparse Decomposition,2,第十六页,共二十七页。,Over-complete dictionary atomsHilbert space:Signal:Error:“Sparse”:lN,satisfy limited error.,SPARSE APPROXIMATION,SPARSE DECOMPOSITION,第十七页,共二十七页。,Major algorithms:Basic Pursuit,Matching Pursuits,OMPMatching Pursuits(MP):1st:kth:,SPARSE DECOMPOSITION,SPARSE APPROXIMATION,与 正交,第十八页,共二十七页。,K-SVD:training dictionaryPotential applications for EEG:Coefficients featuresERP detectionAbnormal EEG detectionClassification of different status of EEG,SPARSE DECOMPOSITION,SPARSE APPROXIMATION,第十九页,共二十七页。,4,Classification algorithms,Common methods,1,第二十页,共二十七页。,Nave BayesLDA:Linear Discriminant AnalysisHMM:Hidden Markov ModellingSVM:Support Vector MachineK-meansANNs:Artificial Neural NetworksFuzzy Logic,COMMON METHODS,第二十一页,共二十七页。,5,Independent Component Analysis,ICA approaches,1,Application,2,第二十二页,共二十七页。,Independent Component Analysis,ICA APPROACHES,APPLICATIONS,第二十三页,共二十七页。,ICA APPROACHES,APPLICATIONS,ICA approaches:Factorizing the joint PDF into its marginal PDFsDecorrelating signals through timeEliminating temporal cross-correlation function,第二十四页,共二十七页。,BSS:Blind Source SeparationNormal brain rhythms,event-related sources Artefacts eye movement&blinking,swallow,APPLICATIONS,ICA APPROACHES,第二十五页,共二十七页。,THANKS!,第二十六页,共二十七页。,内容(nirng)总结,EEG SIGNAL PROCESSING。Optimal KLT(Karhunen-Love)。Yule-Walker aryule(x,p)。Covariance method arcov(x,p),armcov(x,p)。Burg arburg(x,p)。Matching Pursuits(MP):。K-SVD:training dictionary。THANKS,第二十七页,共二十七页。,

此文档下载收益归作者所有

下载文档
你可能关注的文档
收起
展开