eeg analysis during hypnagogium petr svoboda laboratory of system reliability faculty of...
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EEG analysis during hypnagogium
Petr SvobodaLaboratory of System ReliabilityFaculty of TransportationCzech Technical University
e-mail: [email protected]
Presented methods
Traditional methodsFourier transform
Parametrical methodsAutoregressive estimator
Nonlinear methods - Chaos theoryDelay-time embedding, Correlation dimension, Takens estimator, State Space dimension, Lyapunov exponents
EEG activity
Name Freq. [Hz]
Deltha 0,5-4Theta 4-8Alpha 8-15Beta 15-35
electric potential of brain‘s neural activity registered on the skelet four basic frequencies
Traditional methods
Signal‘s stationarity frequency resolution leakage of frequency spectraquality of the spectral estimatephase of the signal is lost
Potencial problems
Estimate of a periodogram using the Fourier transform
Parametric model
Autoregressive (AR) model:
Approximation of an EEG signal by adequate parametric model
Approximation of an EEG signal by linear time invariant filter with transfer function H(z)=1/A(z)
Whitening of signal by AR filter:
Analyzed signal
Pole placement
Autocovariance function
Spectral estimate
Comparison of traditional and parametric methods
Parametric methods:
+ frequency resolution+ parametric description of analyzed signal - estimate of AR model order - high noise sensitivity
Traditional methods:
+ low noise sensitivity - frequency resolution
Microsleep classification
+ Alpha, deltha and theta activity of spectral estimate
Traditional methods
Parametrical methods+ Alpha, deltha and theta activity of spectral estimate- estimate of AR model order- placement of poles in a complex plane
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%
Relaxation
Drowsiness
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
Delay-time embedding
Si=[x(i),x(i+L),… x(i+(m-1)L)]
L… time delaySi… state-space vectorm… state dimensionx… analyzed signal
Selection of Delay Time L
Time delay should be set so, x(i),x(i+L),… are independent
autocorrelation methodmethod of Mutual Information (MI)
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
Microsleep classification
+ matematical description of state-space trajectory reconstructionnonlinearity detection
- correlation dimension D2 estimate
+ Takens estimator+ largest Lyapunov exponents
Chaos theory
Largest Lyapunov Exponents
Sensitive dependence on initial conditions