Lectures videos by Dr.Wim van Drongelen, University of Chicago Book used: Signal Processing for Neuroscientists by Wim van Drongelen Course name: Modeling and Signal Analysis for Neuroscientists
My lecture on the Hilbert Transform and its application can be found on channel Wim van Drongelen ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-MBEQaCvGz2I.html
At request here is the overview for all topics and video lectures and Chapters in the 2nd Edition Text: Lecture 1 Introduction: Signals, Measurement (CH 1 and 2) Lecture 2 Measurement and Noise (CH2 and 3) Lecture 3 Signal Averaging, Time and Frequency Domain Analysis (CH 4 and 5) Lecture 4 Frequency Domain Analysis: The Real and Complex Fourier Series (CH 5) Lecture 5_1 Frequency Domain Analysis: The Fourier Transform and Power Spectrum (CH 6 and 7) Lecture 5_2 2D-Fourier Transform & applications to medical imaging (CH 7) Lecture 6 Frequency Domain Analysis: The Power Spectrum, Lomb's Algorithm and Multi-Taper Estimate (CH 8) Lecture 7 LTI Systems, Convolution, Correlation, and Coherence (CH 13) Lecture 8 (with J. Suresh, S. Canavan) Examples of Correlation and Coherence. Laplace and zTransforms (CH 12 and 13) Lecture 9 Filters 1: Introduction and Demonstration (CH 15 and 16) Lecture 10 Filters 2: Filter Specifications and Digital Filters (CH 17 and 18) Lecture 11 Kalman Filter (CH 19) Lecture 12 Wavelet Analysis (CH 21 and 22) Lecture 13 Wavelet Analysis, Nonlinear Systems (CH 22 and 27) Lecture 14 Volterra Series (CH 11 and 24) Lecture 15 Volterra and Wiener Series (CH 24 and 25) Lecture 16 Wiener Series (CH 25 and 26) Lecture 19 (by Prof. J D Cowan) The Wilson-Cowan Equations (CH 30) Lecture 21 Bifurcations (CH 23) Lecture 22 Ordinary Differential Equations (CH 9 and 10) Lecture 28 Principal Component Analysis and its Applications (CH 28)
The current video refers to the 1st editions of both textbooks. I will now add the reference to the 2nd edition of the textbook that includes all material. This Video is also CH 1 and 2 in the 2nd Edition.
Hello! Thank you for sharing your fantastic course. One question, is there any open courseware for this course? (exercises, notes, tests, exams, etc)? I was wondering if this course can be done individually
@@ElectricTeaCup the only way to follow the course, other than using the RU-vid videos, is as a U of Chicago or IIT student, sorry. There are exercises in the 2nd Ed. Of the book and more material plus selected answers on the book’s website.
Stanislav, The currents we talk about here are not at the level of the electric chair, and indeed not harmful in that sense. However, even small currents could affect the properties you are trying to record and that's not what you want for an objective measurement. Therefore, (since without current measurements cannot be made) it's a good idea to minimize currents in a recording device. WvD
As someone who doesn't understand electronics.. Why is current annoying? It's the source of information for our detectors, so how does it harm exactly?
Hello, because in this exemple we are trying to measure the voltage V not the current, a significant current will dissipate in the resistor and will cause a decrease in the voltage by Ro*I ( V=Ro*I+V') . If we want to measure the current we will use a different setup.
An update of the textbook is available May 2018. See webpage www.elsevier.com/books/signal-processing-for-neuroscientists/drongelen/978-0-12-810482-8 or my lab webpage epilepsylab.uchicago.edu/page/teaching
Correction: Note that I made a mistake on the black board by omitting a factor of 1/2pi for the CIFT equation. [Since omega is the radial frequency, this factor is required (Wim van Drongelen)]
