EEG-signal-processing-脑电信号处理方法算法PPT课件

Sparse decomposition
Over-complete dictionary atoms
Hilbert space H RN : D dk , k 1, 2,...K K N
Signal: Error:
yH
yl drr rIl
l
(
y,
D)
inf yl
y yl
“Sparse”: l<<N , satisfy limited error .
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9
basic features
Modern methods
Frequency Analysis Suboptimal DFT, DCT, DWT; Optimal KLT (Karhunen-Loève)
Demerits: complete statistical information, no fast calculation.
y y, dr0 dr0 R1 y
kth :
Rk y, drk1 supi(1,2,...k ) Rk y, di
y
k
n0Rn y, drndrnRk1 y
d 与 rn Rk1 y 正交
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18
Sparse approximation
sparse decomposition
K-SVD: training dictionary Potential applications for EEG:
signal generation system
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5
bioelectricity
Nonlinear Model
signal generation system
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6
2
Available features
1 Basic features 2 Modern methods
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7
basic features
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12
Basic features
modern methods
AR coefficients estimation methods
Covariance method arcov(x,p), armcov(x,p) Merits: without window good resolution of PSD
Application decomposition, classification, filtering, denoising, whitening.
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15
3
Sparse Representation
1 Sparse Approximation
2 Sparse Decomposition
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16
sparse approximation
Modern methods
Temporal Analysis Signal Segmentation: label the EEG signals by segments of similar
characteristics.
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8
basic features
Temporal Criteria
Modern methods
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
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10
Basic features
modern methods
Signal Parameter Estimation AR model: Merits: Outperform DFT in frequency accuracy. Demerits: suffer from poor estimation of parameters. Improvements: accurate order & coefficients.
Coefficients features ERP detection Abnormal EEG detection Classification of different status of EEG
Demerits: slow
Burg arburg(x,p)
Merits: accurate approximation of PSD
Demerits: line skewing & splitting
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13
Basic features
Comparison
modern methods
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14
Basic features
EEG SIGNAL PROCESSING
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1
Content
1 EEG signal modelling
2 Available features 3 Sparse Representation
4 Classification algorithms 5 Independent Component Analysis
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17
Sparse approximation
sparse decomposition
Major algorithms: Basic Pursuit, Matching Pursuits, OMP
Matching Pursuits (MP):
1st:
y, dr0 supi(1,...k) y, di
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2
1
EEG signal modelling
1 Bioelectricity
2 Signal generation system
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3
bioelectricity
Excitation model
Signal generation system
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4
bioelectricity
Linear Model
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11
Basic features
AR coefficients estimation methods Yule-Walker aryule(x,p)
modern methods
Merits: Toeplitz matrix Levinson-Durbin, fastest!!!
Demerits: with window bad resolution of PSD