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</head><body bgcolor="#ffffff"><font style="font-family: Courier New,Courier,mono;" size="2"><b>Matching Pursuit and Unification in EEG Analysis<br></b></font><a name="contents"><font style="font-family: Courier New,Courier,mono;" size="2">EEG Analysis. Parameterization of EEG Transients.
Epileptic Seizures. Event-Related Desynchronization and Synchronization.
Selective Estimates of Energy. Spatial Localization of Cerebral Sources.</font><br><br></a><font style="font-family: Courier New,Courier,mono;" size="2"><a name="author"><b>Author Bio</b></a></font>
<p style="font-family: Courier New,Courier,mono;">
</p>
<a name="author"><font size="2"><b style="font-family: Courier New,Courier,mono;"><i>Piotr J.
Durka</i></b><span style="font-family: Courier New,Courier,mono;"> is an assistant
professor at Warsaw University`s Institute of Experimental Physics, Department
of Biomedical Physics. He is the author of three books, several articles in
peer-reviewed journals, and chapters in monographs and encyclopedias. Dr. Durka
</span></font><span style="font-size: 10pt; font-family: "Times New Roman";"><font size="2"><span style="font-family: Courier New,Courier,mono;">introduced the matching pursuit
algorithm into the field of biomedical signals analysis. He holds an M.Sc., Ph.D. and Dr. Hab. in physics.</span><br style="font-family: Courier New,Courier,mono;">
<br style="font-family: Courier New,Courier,mono;">
</font><span style="font-family: Courier New,Courier,mono;"><font size="2">www.durka.i</font>nfo</span><br style="font-family: Courier New,Courier,mono;">
<span style="font-family: Courier New,Courier,mono;">www.eeg.pl</span></span></a><br><strong>Tad Ulrych napisał(a):</strong><br><blockquote style="border-left: 2px solid rgb(0, 0, 255); padding-right: 0px; padding-left: 5px; margin-left: 5px; margin-right: 0px;">
<div><font face="Arial" size="2"><font size="3"> </font>
<div><font face="Times New Roman" size="2"><strong>Hi Joe</strong></font></div>
<div><font face="Times New Roman" size="2"><strong></strong></font> </div>
<div><font face="Times New Roman" size="2"><strong>I enclose a reference to a book
that you might find useful. Not EEG based, although it does have
a smattering of blind deconvolution via ICA, but it
is replete with signal processing and inversion. The fact that it is
directed towards geophysical data should not deter those who are interested in
processing and inversion since these subjects are ubiquitous. My favorite
reading is in the fields of others external to mine.</strong></font></div>
<div><font face="Times New Roman" size="2"><strong>You mention that you took only
one class in DSP. The first 3 chapters stem from Mauricio and my experience in
teaching undergraduate and graduate courses for about 400 years (in my case in
particular). Your interest in spectral analysis is covered very fully in the
text.</strong></font></div>
<div><font face="Times New Roman" size="2"><strong>Forgive me for
this self propaganda, but Elsevier is not Springer (and the book is
useful). </strong></font></div>
<div><font face="Times New Roman" size="2"><strong></strong></font> </div>
<div><font face="Times New Roman" size="2"><strong>Regards and fortune in your
search and career.</strong></font></div>
<div><font face="Times New Roman" size="2"><strong></strong></font> </div>
<div><font face="Times New Roman" size="2"><strong>Tad</strong></font></div>
<div><strong><font face="Times New Roman"></font></strong> </div>
<div><strong><font face="Times New Roman"></font></strong> </div>
<div>
<p><font face="Times New Roman"><font size="4"><strong>INFORMATION-BASED INVERSION AND
PROCESSING</strong></font></font></p>
<p><font face="Times New Roman"><font size="4"><strong>WITH
APPLICATIONS</strong></font></font></p>
<p><font face="Times New Roman" size="4"> </font></p>
<p><font face="Times New Roman" size="4"> </font></p>
<p><font face="Times New Roman"><font size="2"><b><font size="3">T. J. Ulrych</font></b>
</font></font></p>
<p><font face="Times New Roman"><font size="2"><font size="2"><font size="2">The </font><font size="2">University</font>
<font size="2">of </font><font size="2">British Columbia</font></font><font size="2">,</font> <font size="2">Vancouver</font><font size="2">,</font> <font size="2">BC</font><font size="2">,</font> <font size="2">Canada</font></font></font><br><font face="Times New Roman"><font size="2"><b>and</b></font></font></p>
<p><font face="Times New Roman"><font size="3"><b><font size="3">M.</font> <font size="3">D.</font> <font size="3">Sacchi</font></b></font></font></p>
<p><font face="Times New Roman"><font size="2"><font size="2">The </font><font size="2">University</font>
<font size="2">of </font><font size="2">Alberta</font><font size="2">,</font> <font size="2"><font size="2">Edmonton</font><font size="2">,</font> </font><font size="2">Al</font><font size="2">berta,</font> <font size="2">Canada</font>
</font></font></p>
<p><font face="Times New Roman"><font size="2"><font size="2"></font></font></font> </p>
<p><font face="Times New Roman"><font size="2"><b>Description</b> </font></font></p>
<p><br><font face="Times New Roman"><font size="2">This book
examines different classical and modern aspects of geophysical data processing
and inversion with emphasis on the processing of seismic records in applied
seismology. <br><br>Chapter 1 introduces basic concepts including: probability
theory (expectation operator and ensemble statistics), elementary principles of
parameter estimation, Fourier and z-transform essentials, and issues of
orthogonality. In Chapter 2, the linear treatment of time series is
</font></font></p>
<p><font face="Times New Roman" size="2">provided. Particular
attention is paid to Wold decomposition theorem and time series models (AR, MA,
and ARMA) and their connection to seismic data analysis problems. Chapter 3
introduces concepts of Information theory and contains a synopsis of those
topics that are used throughout the book. Examples are entropy, conditional
entropy, Burg's maximum entropy spectral estimator, and mutual information.
Chapter 4 provides a description of inverse problems first from a deterministic
point of view, then from a probabilistic one. Chapter 5 deals with methods to
improve the signal-to-noise ratio of seismic records. Concepts from previous
chapters are put in practice for designing prediction error filters for noise
attenuation and high-resolution Radon operators. Chapter 6 deals with the topic
of deconvolution and the inversion of acoustic impedance. The first part
discusses band-limited extrapolation assuming a known wavelet and considers the
issue of wavelet estimation. The second part deals with sparse deconvolution
using various 'entropy' type norms. Finally, Chapter7 introduces recent topics
of interest to the authors.
<br><br>The emphasis of this book is on applied seismology but
researchers in the area of global seismology and signal processing and inversion
in general will find material that is relevant to the ubiquitous problem of
estimating complex models from a limited number of noisy observations. This book
is also particularly well suited for graduate students who are interested in the
fields of data processing and inversion.<br></font><a name="audience"></a><br><a name="bibliographicinfo"></a><font face="Times New Roman"><font size="2"><b>Bibliographic
& ordering Information</b> <br>Hardbound, 436 pages, publication
date: DEC-2005<br>ISBN-13: 978-0-08-044721-6<br>ISBN-10:
0-08-044721-X<br>Imprint: ELSEVIER<br><b>Price:</b> <br>USD 85<br>EUR 76.95<br>GBP
52.99</font></font></p></div></font></div>
<div><font face="Arial" size="2"></font> </div>
<blockquote>
<div>----- Original Message ----- </div>
<div><b>From:</b>
<a title="joe.mellet@gmail.com" href="mailto:joe.mellet@gmail.com">Joe
Mellet</a> </div>
<div><b>To:</b> <a title="eeglablist@sccn.ucsd.edu" href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a> </div>
<div><b>Sent:</b> Monday, July 02, 2007 8:48 AM</div>
<div><b>Subject:</b> [Eeglablist] Textbook
Suggestions</div>
<div><br></div>Hey All,<br>I was wondering if there are any suggestions for a
book that reviews the basics of signal processing, especially helpful would be
one that applies DSP principles to EEG signals. I just graduated with a
degree in bioengineering from Penn State, but I only took one class on signal
processing and could use a heck of a lot of review before I proceed with power
spectral analysis on sleep EEG records. Thanks! <br><br>Joe
Mellet<br>Research Assistant<br>Unit for Experimental Psychiatry<br>Division
of Sleep and Chronobiology<br>University of Pennsylvania School of
Medicine<br>1033 Blockley Hall<br>423 Guardian Drive<br>Philadelphia, PA
19104-6021 <br>Phone: (215) 573-3237 <br><br><br>
<p>
</p><hr>
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