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Functional Brain Signal Processing: EEG & fMRI Lesson 5

This course explores the principles and techniques of analyzing and processing functional brain signals, specifically EEG and fMRI. It covers important Event-Related Potentials (ERPs) such as N100 and P300, as well as spectral estimation methods and ARMA modeling.

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Functional Brain Signal Processing: EEG & fMRI Lesson 5

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  1. M.Tech. (CS), Semester III, Course B50 Functional Brain Signal Processing: EEG & fMRILesson 5 Kaushik Majumdar Indian Statistical Institute Bangalore Center kmajumdar@isibang.ac.in

  2. Sur & Sinha, Ind. J. Psychiatr., 18(1): 70 – 73, 2009; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/ Some Important ERPs • N100: A negative deflection peaking between 90 and 200 msec after the onset of stimulus, is observed when an unexpected stimulus is presented. It is an orienting response or a “matching process,” that is, whenever a stimulus is presented, it is matched with previously experienced stimuli. It has maximum amplitude over Cz and is therefore also called “vertex potential.”

  3. Sur & Sinha, Ind. J. Psychiatr., 18(1): 70 – 73, 2009; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/ Important ERPs (cont.) • P300: The P3 wave was discovered by Sutton et al. in 1965 and since then has been the major component of research in the field of ERP. For auditory stimuli, the latency range is 250-400 msec for most adult subjects between 20 and 70 years of age. The latency is usually interpreted as the speed of stimulus classification resulting from discrimination of one event from another.

  4. Sur & Sinha, Ind. J. Psychiatr., 18(1): 70 – 73, 2009; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/ Important ERPs (cont.) Shorter latencies indicate superior mental performance relative to longer latencies. P3 amplitude seems to reflect stimulus information such that greater attention produces larger P3 waves. A wide variety of paradigms have been used to elicit the P300, of which the “oddball” paradigm is the most utilized where different stimuli are presented in a series such that one of them occurs relatively infrequently — that is the oddball.

  5. Sur & Sinha, Ind. J. Psychiatr., 18(1): 70 – 73, 2009; http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016705/ Important ERPs (cont.) The subject is instructed to respond to the infrequent or target stimulus and not to the frequently presented or standard stimulus. Reduced P300 amplitude is an indicator of the broad neurobiological vulnerability that underlies disorders within the externalizing spectrum {alcohol dependence, drug dependence, nicotine dependence, conduct disorder and adult antisocial behavior} (Patrick et al., 2006).

  6. http://pubs.niaaa.nih.gov/publications/arh313/238-242.htm ERP Plot by Time Frequency Diagram

  7. Spectral Estimation • Spectral estimation can be achieved by Fourier transform or by autoregressive models (there are other methods also, see Chapter 14 of Digital Signal Processing: Principles, Algorithms & Applications, 4th ed., by J. G. Proakis and D. J. Manolakis, Pearson, 2007).

  8. http://paulbourke.net/miscellaneous/windows/ Spectral Estimation by Fourier Transform (Nonparametric) Welch window Continuous form Discrete form is the power associated with frequency n. W() is a window function

  9. Limitations • Long data length required. • Spectral leakage masks weak signals. • Poor frequency resolution. • Signal has to be stationary (statistical properties do not change over time).

  10. Gaussian (White) Noise • A Gaussian noise signal is with 0 mean. • It is wide sense stationary, that is if the digitized Gaussian white noise signal is ω(n) then E[ω(n)ω*(n + k)] = 0 for all k > 0 (k is nonnegative). • In addition it can be made to have unit variance.

  11. Spectral Estimation by Autoreg-ressive Moving Average (ARMA) (Parametric) Has following advantages: • Suitable for short data length. • Gives better frequency resolution. • Avoids spectral leakage.

  12. ARMA (cont.) H(z) is the system function of ARMA(p,q), where (p,q) is the model order. Corresponding difference equation with y(n) output and x(n) input. (1) Autoregressive (AR) Moving average (MA)

  13. Proakis and Manolakis, 2007, p. 987 ARMA (cont.) For a Gaussian white noise signal, is variance of the noise x and is Dirac delta function. For the power spectrum of a general signal x, where ω is a Gaussian white noise signal.

  14. Gray et al., Nature, 338: 334 – 337, 23 March 1989 Autocorrelation & Crosscorrelation

  15. ARMA (cont.) • In an ARMA model if A(z) = 1 then H(z) = B(z) and the model reduces to moving average (MA) process of order q. • In an ARMA model if B(z) = 1 then H(z) = 1/A(z) and the model reduces to autoregressive (AR) process of order p.

  16. Proakis and Manolakis, 2007, p. 987 Wold’s Decomposition Theorem • Any ARMA or MA process can be represented uniquely by an AR model of possibly infinite order. • Any ARMA or AR process can be represented by an MA model of possibly infinite order.

  17. Model Parameters of ARMA Given by Yule-Walker Equations Here m > q. m is the frequency at which power spectrum is to be determined.

  18. Yule-Walker Equations (cont.) Yule-Walker equations give the model parameters for the AR part in the ARMA model.

  19. Proakis and Manolakis, 2007, p. 990 Model Parameters for the MA Part

  20. Proakis and Manolakis, 2007, p. 837 Derivation of Yule-Walker Equations • When the power spectral density of the stationary random process is a rational function, there is a basic relationship between the autocorrelation sequence and the model parameters and . This is given by Yule-Walker equations.

  21. Derivation (cont.) • ARMA difference equation (1) for input as white Gaussian noise and output x is • Multiplying both sides by x*(n - m) and taking expectation we get

  22. Proakis and Manolakis, 2007, p. 837 Derivation (cont.) (2) While

  23. Derivation (cont.) Since ω is white noise, in the last step we have used (3) Combining (2) and (3) we get

  24. Derivation (cont.)

  25. Probability Density Function Estimation • Probability density function estimation of a one dimensional data set is very similar to power spectrum estimation. The only difference is that for a PDF the integral over the whole space will have to be unity, whereas power spectrum estimation does not have any such constraint. • See for detail “Model-based probability density function estimation,” S. Kay, IEEE Sig. Proc. Lett., 5(12): 318 – 320, 1998.

  26. References • Digital Signal Processing: Principles, Algorithms & Applications, 4th ed., J. G. Proakis and D. J. Manolakis, Pearson, 2007. Chapter 14. • Modern Spectral Estimation: Theory & Application, S. M. Kay, Pearson, 1988, for a general perusal of nonparametric spectral estimation and probability distribution function estimation methods beyond Yule-Walker equations.

  27. THANK YOUThis lecture is available at http://www.isibang.ac.in/~kaushik

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