A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Parametric modeling. <s> Preface. Digital biosignal processing: a selective and subjective introduction. List of contributors. Chapter 1. Origins of the main human biosignals (R. Schandry). Chapter 2. Analog signal conditioning (T. Elbert). Chapter 3. Biosignal data acquisition (M. Buhrer and B. Sparrer). Chapter 4. The foundations of digital Fourier analysis (N.L. Hesselmann). Chapter 5. Digital signal conditioning (W. Wolf). Chapter 6. Model-based analysis of neurophysiological signals (B. Kemp and F.H. Lopes da Silva). Chapter 7. Time series analysis by means of linear modeling (B.H. Jansen). Chapter 8. Point processes. A signal processing approach to the analysis of event series in biomedical applications (G. Baselli and S. Civardi). Chapter 9. Models for estimation and removal of artifacts in biological signals (J.L. Kenemans, P.C.M. Molenaar and M.N. Verbaten). Chapter 10. Adaptive signal processing (J.I. Aunon and Z. Keirn). Chapter 11. Real time processing of event-related potentials (J.P. Rosenfeld). Chapter 12. Instrumental behavior shaping automata (G. Silverman and B.R. Dworkin). Chapter 13. Curvefitting (A.T. Johnson). Chapter 14. Wavelet detection and classification (A. Cohen). Chapter 15. Relationships among signals: cross-spectral analysis of the EEG (G. Dumermuth and L. Molinari). Chapter 16. Multivariate methods in biosignal analysis: application of principal component analysis to event-related potentials (J. Mocks and R. Verleger). Chapter 17. Mapping procedures (G. Pfurtscheller). Chapter 18. Digital image processing (K.D. Brinkmann, H.W. Eisermann and W. Heyne). Chapter 19. Neural network methods (I. Leuthausser). Subject index. <s> BIB001 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Parametric modeling. <s> In this study we compared two spectral estimation methods for feature extraction in development of computer access for communication and control based on electroencephalographic (EEG) signals. We compared power spectrum calculated by fast Fourier transform (FFT) and auto regressive (AR) coefficients calculated by Burg's algorithm. We analyzed four surface EEG signals recorded above sensory- motor areas, while the subject was attempting to use only mental activities to modulate his EEG signals resulting in desired simple movements of animated object on the feedback computer screen. To establish communication channel based on EEG signals, we asked our subject to determine which mental activity produced reproducible control actions. During the on- line training we used AR coefficients of recorded EEG signals for the feedback generating variables, i.e. the object's movement direction and speed were dependent on the EEG pattern. Our subject was able to determine which mental activity resulted in desired movements and to learn to control object movements with more than 90% accuracy. We performed off-line analysis to determine if FFT or AR can be more successful in extracting relevant information from the EEG signals before performing pattern recognition by a machine learning classifier. Probability of misclassification was used as the outcome measure. Off-line analysis showed that the overall best classification accuracy achieved with AR exceeds that achieved with FFT. <s> BIB002
Parametric approaches assume the time series under analysis to be the output of a given linear mathematical model. They require an a priori choice of the structure and order of the signal generation mechanism model BIB001 ). The optimum model order is best estimated not only by maximizing the fitness but also by limiting the model's complexity. For noisy signals, if the model's order is too high, spurious peaks in the spectra will result. On the other hand, if the order is too low, smooth spectra are obtained  BIB002 BIB001 . For short EEG segments, parametric modeling results in better frequency resolution and a good spectral estimate. Note that parametric modeling may yield poor estimates if the length of the EEG segments processed is too short (Birch 1988) . For such modeling, there is no need for a priori information about potential frequency bands, and there is no need to window the data in order to decrease the spectral leakage. Also, the frequency resolution does not depend on the number of data points (Guger et al 2003a BIB002 BIB001 . Estimating these parameters, however, is very sensitive to artifacts (Birch 1988 , Guger et al 2003a . Special attention should be paid to the choice of the sampling rate in parametric modeling BIB001 , since severely oversampled signals tend to show only very small amplitude differences between successive samples. Hence, low-order models produce small prediction errors, giving the false illusion that an adequate model has been obtained. The sampling rates dictated by the Nyquist criterion are recommended.