1. Signal Processing in Neuroinformatics EEG Signal Processing Yongnan Ji

2. Modeling the EEG signal. Artifacts in the EEG. Nonparametric Spectral Analysis. Model-based spectral Analysis.

3. 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

4. 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

5. 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

6. 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.

7. Performance and Paramerters 3. Performance. Choosing method. Model order. Sampling rate. 4. Parameters.

8. Exercise 3.7 Fourier Transform

9. Exercise 3.7 Inverse-Fourier Transform