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Modeling the EEG signal. Deterministic VS Stochastic Linear Stochastic Models Nonlinear Modelling of EEG ARMA, AR Time-varing AR modelling Multivariate AR modelling AR modelling with impulse input
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Signal Processing in Neuroinformatics
EEG Signal Processing
Yongnan Ji
Modeling the EEG signal.
Artifacts in the EEG.Nonparametric Spectral Analysis.Model-based spectral Analysis.
Modeling the EEG signal.
Deterministic VS Stochastic
Linear Stochastic ModelsNonlinear Modelling
of EEG
ARMA, AR
Time-varing AR modelling
Multivariate AR modelling
AR modelling with impulse input
Artifacts in the EEG.
Types of artifacts usually metEye movement and blinks, Muscle activity, Cardiac activity, Electrodes and equipment
Artifact ProcessingAdditive noise or multiplicative noise
How to deal with the artifact? Artifact Reduction Using Linear Filtering. Artifact Cancellation Using Linear Combined Reference
Signals. Adaptive Artifact Cancellation Using Linearly Combined
Reference Signals Artifact Cancellation Using Filtered Reference Signals
Nonparametric Spectral Analysis
We can calculate an estimation of the power spectrum from the samples of the signal:
Mean and variance of the estimation changes against the selection of windows.
2. Spectral Parameters Spetral slope. Hjorth descriptors. Spectral Purity Index.
1. Fourier-based Power Spectrum Analysis
Model-based Spectral Analysis
1. Variance of the input noise.2. Methods to find the coefficients of the linear
algorithm: The Autocorrelation/Covariance Methods:
Minimization of the error variance. The Modified Covariance Method:
The variance is calcutated taking into acount backward prediction error.
Burg’s Method: We explicitly make use of the recursion method. Estimation with lattice structure.
Performance and Paramerters
3. Performance. Choosing method. Model order. Sampling rate.
4. Parameters.
Exercise 3.7
)()()( xfxmxg
)()()( FMG
hz9))2(sin()(
o
o
fxfF F
Fourier Transform
)(G
9 0 9
99 0 810 108
Exercise 3.7
)(M1
0 1
hz1),2sin()( mm fxfxM
Inverse-Fourier Transform