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 met

Eye movement and blinks, Muscle activity, Cardiac activity, Electrodes and equipment

Artifact Processing

Additive 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

f

xfF F

Fourier Transform

)(G

9 09

99 0 810 108

Exercise 3.7

)(M1

01

hz1),2sin()( mm fxfxM

Inverse-Fourier Transform