EEG-signal-processing-脑电信号处理方法算法

1、EEG SIGNAL PROCESSING第一页，共二十七页。EEG signal modelling1Available features2Classification algorithms3Independent Component Analysis4ContentSparse Representation5第二页，共二十七页。1EEG signal modellingBioelectricity 1Signal generation system2第三页，共二十七页。 bioelectricitySignal generation systemExcitation model第四页，共二

2、十七页。 signal generation systembioelectricityLinear Model第五页，共二十七页。 signal generation systembioelectricityNonlinear Model第六页，共二十七页。2Available featuresBasic features1Modern methods2第七页，共二十七页。Temporal Analysis Signal Segmentation: label the EEG signals by segments of similar characteristics. basic featu

3、resModern methods第八页，共二十七页。Temporal Criteria basic featuresModern 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 featuresModern methods第十页，共二十七页。Signal Parameter Estimation AR model: Merits

4、: Outperform DFT in frequency accuracy. Demerits: suffer from poor estimation of parameters. Improvements: accurate order & coefficients. modern methodsBasic 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

5、window bad resolution of PSD modern methodsBasic 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

6、 methodsBasic features第十三页，共二十七页。 modern methodsBasic featuresComparison 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 decompositio

7、n, classification, filtering, denoising, whitening. modern methodsBasic features第十五页，共二十七页。3Sparse RepresentationSparse Approximation1Sparse Decomposition 2第十六页，共二十七页。Over-complete dictionary atomsHilbert space : Signal: Error: “Sparse: lN , satisfy limited error . sparse approximationSparse decompo

8、sition第十七页，共二十七页。Major algorithms: Basic Pursuit, Matching Pursuits, OMPMatching Pursuits (MP):1st: kth : sparse decompositionSparse 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 differe

9、nt status of EEG sparse decompositionSparse approximation第十九页，共二十七页。4Classification algorithmsCommon methods1第二十页，共二十七页。Nave BayesLDA: Linear Discriminant AnalysisHMM: Hidden Markov ModellingSVM: Support Vector MachineK-meansANNs: Artificial Neural NetworksFuzzy LogicCommon methods第二十一页，共二十七页。5Indep

10、endent Component AnalysisICA approaches1Application 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 applicationsICA 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 applicationsIca approaches第二十五页，共二十七页。THANKS!第二十六页，共二十七页。内容总结EEG SIGNAL PROCESSING。Optimal KLT