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Authors: Weitkunat, R.  
 
Binding: HC  
 
Pages: 602  
 
Publication Date: 6 1, 1991  
 
Publisher: Elsevier  
 
Hardbound. Digital Signal Processing (DSP) is the fundamental tool of biomedical data analysis, just as a telescope is in astronomy. Assuming data has been correctly gathered according to a well-designed protocol, the effectiveness of the application of DSP techniques determines the success of a study. Unfortunately, since DSP is a relatively new branch of electrical engineering and applied mathematics, and is not usually included in the curricula of psychological, biological or medical science educational programs, the vast majority of biomedical researchers are inadequately prepared in DSP, and are thus at a severe disadvantage in conducting their research.  
 
The problem is not easily rectified since DSP is a technically complex area to study, which, at the least, requires prerequisite knowledge of linear algebra, calculus and the physics of electricity. But without at least a working knowledge of common DSP procedures, researchers are reduced to rote  
 
Review:...a very thoughtful primer...this book succeeds amazingly well in providing a solid introduction to digital processing...,(EEG AND CLIN. NEUROPHYSIOLOGY)  
 
Table of Contents: 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. B & uuml; hrer 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. M & ouml; cks 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. Leuth & auml; usser).  
Subject index.  
 
Book Series: Techniques in the Behavioral and Neural Sciences