1 / 18

EEG analysis during hypnagogium

EEG analysis during hypnagogium. Petr Svoboda Laboratory of System Reliability Faculty of Transportation Czech Technical University e-mail: svobodap@spel.cz. Presented methods. Traditional methods Fourier transform Parametrical methods Autoregressive estimator

kimo
Download Presentation

EEG analysis during hypnagogium

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. EEG analysis during hypnagogium Petr Svoboda Laboratory of System Reliability Faculty of Transportation Czech Technical University e-mail: svobodap@spel.cz

  2. Presented methods • Traditional methods Fourier transform • Parametrical methods Autoregressive estimator • Nonlinear methods - Chaos theory Delay-time embedding, Correlation dimension, Takens estimator, State Space dimension, Lyapunov exponents

  3. EEG activity • electric potential of brain‘s neural activity • registered on the skelet • four basic frequencies

  4. Traditional methods Estimate of a periodogram using the Fourier transform Potencial problems • Signal‘s stationarity • frequency resolution • leakage of frequency spectra • quality of the spectral estimate • phase of the signal is lost

  5. Parametric model Approximation of an EEG signal by adequate parametric model Autoregressive (AR) model: Approximation of an EEG signal bylinear time invariant filter with transfer function H(z)=1/A(z) Whitening of signal by AR filter:

  6. Analyzed signal Pole placement Autocovariance function Spectral estimate

  7. Comparison of traditional and parametric methods Traditional methods: + low noise sensitivity - frequency resolution Parametric methods: + frequency resolution + parametric description of analyzed signal - estimate of AR model order - high noise sensitivity

  8. Microsleep classification Traditional methods + Alpha, deltha and theta activity of spectral estimate Parametrical methods + Alpha, deltha and theta activity of spectral estimate - estimate of AR model order - placement of poles in a complex plane

  9. Classification by spectral estimate • Classification into 2 states • RELAXATION • DROWSINESS • Classification based on neural network (back propagation) • Classical methods: accuracy of about 87% • Parametrical methods: accuracy of about 90%

  10. Relaxation Drowsiness

  11. Chaos theory • analysis of dynamic deterministic systems • high sensitivity on initial conditions • known dynamics and phase of the system • detecting nonlinearity by surrogate data testing • delay-time embedding • state-space dimension estimate • estimate of delay time • estimate of fractal dimension D2 • Takens estimator for D2 dimension • largest Lyapunov exponents

  12. Delay-time embedding Si=[x(i),x(i+L),… x(i+(m-1)L)] L… time delay Si… state-space vector m… state dimension x… analyzed signal

  13. Selection of Delay Time L Time delay should be set so, x(i),x(i+L),… are independent • autocorrelation method • method of Mutual Information (MI)

  14. Fractal dimension & Takens estimator Fractal dimension is a measure of complexity of the analyzed signal D2 = log C(r) / log r Where C(r) is correlation integral D2 computed by maximum likelihood estimator is known as Takens estimator

  15. Microsleep classification Chaos theory + matematical description of state-space trajectory reconstruction nonlinearity detection - correlation dimension D2 estimate + Takens estimator + largest Lyapunov exponents

  16. Largest Lyapunov Exponents Sensitive dependence on initial conditions

More Related