# Analysis of Human EEG Data

## Presentation on theme: "Analysis of Human EEG Data"— Presentation transcript:

Analysis of Human EEG Data
Pavel Stránský Supervisor: Prof. RNDr. Petr Šeba, DrSc.

Content Measurement and structure of EEG signal
EEG as a multivariate time series, statistical approach to EEG data processing Small introduction to random matrices theory My present results and outlook

Measurement and Structure of EEG Signal
1 Measurement and Structure of EEG Signal

1. Measurement and Structure of EEG Signal
Cerebral Electric Activity EEG = Electro-encephalography, Electro-encephalogram

1. Measurement and Structure of EEG Signal
Location of the Electrodes (10-20 system, 21 electrodes)

1. Measurement and Structure of EEG Signal
An Example of EEG Measurement Alpha waves Beta, theta, delta waves Other graphoelements Artefacts

Statistical Approach to EEG Data
2. Statistical Approach to EEG Data

2. Statistical Approach to EEG Data
Modelling and processing time series Vector Autoregression VAR(p) Stacionarity (Covariance – stacionarity): for all t and any j White noise: for all t, t1, t2

2. Statistical Approach to EEG Data
Modelling and processing time series (cont.) Other ways of treating with time series: Principal component analysis Independent component analysis Testing for periodicity (Fisher’s test, Siegel’s test) mixing ICA

3. Small introduction to random matrix theory (RMT)

3. Small introduction to RMT
Random matrices Study of excitation spectra of compound nuclei The same behaviour like eigenvalues of random matrices 3 principal ensembles: GOE, GUE, GSE Hermitian self-dual matrices, symplectic transformations Hermitian matrices, unitary transformations Def: Gaussian othogonal ensemble is defined in the space of real symmetric matrices by two requirements: 1. Invariance (O is orthogonal matrix) 2. Elements are statistically independent which means that , where (probablity density function)

3. Small introduction to RMT
Random matrices (cont.) Universality classes: GUE Hamiltonians without time reversal symmetry GOE Hamiltonians with time reversal symmetry and WITHOUT spin-1/2 interactions GSE Hamiltonians with time reversal symmetry and WITH spin-1/2 interactions Universal law for joint probability density function: For energies x(eigenvalues of H) b = 1 GOE b = 2 GUE b = 4 GSE

3. Little introduction to RMT
Random matrices (cont.) Spectral correlations (nearest neighbour spacing distribution): Wigner distribution Normalization

3. Little introduction to RMT
Random matrices (cont.) Other distributions (taking into account correlations for longer distances) S2 statistics (number variance) D3 statistics (spectral rigidity)

4. Results, outlook

Correlation analysis of EEG Data
4. Results, outlook Correlation analysis of EEG Data Dividing EEG signal from M channels x1, ..., xM into cells of constant time length T Computing correlation matrix Cm for the mth cell with normalizing mean and variance: Finding eigenvalues xm of all correlation matrices Cm

Correlation analysis (cont.)
4. Results, outlook Correlation analysis (cont.) Unfolding the spectra: (after unfolding all eigenvalues are "equally important", the resulting eigenvalue density r(x) is constant) Finding nearest neighbour distribution p(s) for the unfolded spectra:

Correlation analysis (cont.)
4. Results, outlook Correlation analysis (cont.) Comparing computed spacing distribution with theoretical Wigner curve

Outlook 4. Results, outlook
Use more subtle method from RMT and time series analysis to analyze the correlations and also autocorrelations (correlations in time) Find significant and reproducible variables for standard EEG measured on healthy subjects Deviations are expected if there was some neural disease

Literature 4. Results, outlook
P. Šeba, Random Matrix Analysis of Human EEG Data, Phys. Rev. Lett. 91, (2003) T. Guhr, A. Müller-Groeling, H. A. Weidenmüller, Random Matrix Theories in Quantum Physics: Common Concepts, Phys. Rep. 299, 189 (1998) M. L. Mehta, Random Matrices and the Statistical Theory of Energy Levels, Academic Press (1967) H. J. Stöckmann, Quantum Chaos: An Introduction, Cambridge University Press (1999) A. F. Siegel, Testing for Periodicity in a Time Series, JASA 75, 345 (1980) J. D. Hamilton, Time Series Analysis, Princeton University Press (1994) A. Jung, Statistical Analysis of Biomedical Data, Dissertation, Universität Regensburg (2003) J. Faber, Elektroencefalografie a psychofyziologie, ISV nakladatelství Praha (2001)