EEG signal processing 脑电信号处理方法算法

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d rn与 Rk 1 y 正交
y n0 Rn y, d rn d rn Rk 1 y
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SPARSE APPROXIMATION
SPARSE DECOMPOSITION
K-SVD: training dictionary Potential applications for EEG:
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EEG signal modelling
Bioelectricity
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Signal generation system
BIOELECTRICITY
SIGNAL GENERATION SYSTEM
Excitation model
BIOELECTRICITY
SIGNAL GENERATION SYSTEM
BASIC FEATURES
MODERN METHODS
AR coefficients estimation methods Covariance method arcov(x,p), armcov(x,p) Merits: without window good resolution of PSD Demerits: slow Burg arburg(x,p) Merits: accurate approximation of PSD Demerits: line skewing & splitting
ICA APPROACHES
APPLICATIONS
BSS: Blind Source Separation Normal brain rhythms, event-related sources Artefacts eye movement & blinking, swallow
THANKS!
ICA approaches: Factorizing the joint PDF into its marginal PDFs Decorrelating signals through time Eliminating temporal cross-correlation function
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D dk , k 1, 2,...K
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l ( y, D) inf y yl
“Sparse”: l<<N , satisfy limited error .
SPARSE APPROXIMATION
SPARSE DECOMPOSITION
Fuzzy Logic
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Independent Component Analysis
ICA approaches
Application
ICA APPROACHES
APPLICATIONS
Independent Component Analysis
ICA APPROACHES
APPLICATIONS
Coefficients features ERP detection
Abnormal EEG detection
Classification of different status of EEG
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Classification algorithms
Common methods
COMMON METHODS
Naï ve Bayes
LDA: Linear Discriminant Analysis
HMM: Hidden Markov Modelling
SVM: Support Vector Machine
K-means
ANNs: Artificial Neural Networks
Signal Segmentation: label the EEG signals by segments of similar characteristics.
BASIC FEATURES
MODERN METHODS
Temporal Criteria
BASIC FEATURES
MODERN METHODS
BASIC FEATURES
MODERN METHODS
Comparison
BASIC FEATURES
MODERN METHODS
Principal Component Analysis Use same concept as SVD Decompose data into uncorrelated orthogonal components Autocorrelation matrix is diagonalized Each eigenvector represents a principal component Application decomposition, classification, filtering, denoising, whitening.
MODERN METHODS
Signal Parameter Estimation AR model: Merits: Outperform DFT in frequency accuracy. Demerits: suffer from poor estimation of parameters. Improvements: accurate order & coefficients.
Major algorithms: Basic Pursuit, Matching Pursuits, OMP Matching Pursuits (MP):
1st:
y, dr0 supi(1,...k ) y, di
y y, dr0 dr0 R1 y
kth :
EEG SIGNAL PROCESSING
CONTENT
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EEG signal modelling Available features Sparse Representation
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Classification algorithms
Independent Component Analysis
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Sparse Representation
Sparse Approximation
Sparse Decomposition
SPARSE APPROXIMATION
SPARSE DECOMPOSITION
Over-complete dictionary atoms Hilbert space H R N : Signal: y H Error:
Linear Model
BIOELECTRICITY
SIGNAL GENERATION SYSTEM
Nonlinear Model
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Available features
Basic features
2Hale Waihona Puke Baidu
Modern methods
BASIC FEATURES
MODERN METHODS
Temporal Analysis
Frequency Analysis Suboptimal DFT, DCT, DWT; Optimal KLT (Karhunen-Loè ve)
Demerits: complete statistical information, no fast calculation.
BASIC FEATURES
BASIC FEATURES
MODERN METHODS
AR coefficients estimation methods Yule-Walker aryule(x,p)
Merits: Toeplitz matrix Levinson-Durbin, fastest!!! Demerits: with window bad resolution of PSD