### Site Tools

analysis:rhythms:step3

# Differences

This shows you the differences between two versions of the page.

 analysis:rhythms:step3 [2016/03/29 15:39]mvdm created analysis:rhythms:step3 [2018/07/07 10:19] (current) 2016/03/29 15:41 mvdm 2016/03/29 15:40 mvdm 2016/03/29 15:40 mvdm 2016/03/29 15:39 mvdm created Next revision Previous revision 2016/03/29 15:41 mvdm 2016/03/29 15:40 mvdm 2016/03/29 15:40 mvdm 2016/03/29 15:39 mvdm created Line 7: Line 7: * Understand the fundamentals of sampling theory: Nyquist frequency, aliasing * Understand the fundamentals of sampling theory: Nyquist frequency, aliasing * Learn why and how to use anti-aliasing filters * Learn why and how to use anti-aliasing filters - * Reconstruct a signal from sampled data + - * Examine the file structure of Neuralynx continuously sampled data in detail + Resources ​(optional): - + - Resources: + * (intuitive background) [[http://​redwood.berkeley.edu/​bruno/​npb261/​aliasing.pdf | nice, quick intro]] to aliasing by Bruno Olshausen, with some connections to the human visual system * (intuitive background) [[http://​redwood.berkeley.edu/​bruno/​npb261/​aliasing.pdf | nice, quick intro]] to aliasing by Bruno Olshausen, with some connections to the human visual system - * (more technical background, optional) read Chapter 3 of the [[analysis:​course-w16|Leis book]]. Skip sections 3.4.3, 3.4.4, 3.4.5, 3.4.6, 3.4.7, 3.7. Skim section 3.6. + * (more technical background) read Chapter 3 of the [[analysis:​course-w16|Leis book]]. Skip sections 3.4.3, 3.4.4, 3.4.5, 3.4.6, 3.4.7, 3.7. Skim section 3.6. ==== Introductory remarks ==== ==== Introductory remarks ==== Line 23: Line 21: ==== Motivating example: aliasing ==== ==== Motivating example: aliasing ==== - Before you begin, do a ''​git pull''​ from the course repository. Also, to reproduce the figures shown here, change the default font size (''​set(0,'​DefaultAxesFontSize',​18)''​ -- a good place to put this is in your path shortcut). + Let's start with an example that illustrates what can go wrong if you are not aware of some basic sampling theory ideas. To do so, we will first construct a 10Hz signal, sampled at 1000Hz ​(i.e. we are taking 1000 measurements per second). Recalling that the frequency //f// of a sine wave is given by $y = sin(2 \pi f t)$: - + - Let's start with an example that illustrates what can go wrong if you are not aware of some basic sampling theory ideas. To do so, we will first construct a 10Hz signal, sampled at 1000Hz. Recalling that the frequency //f// of a sine wave is given by $y = sin(2 \pi f t)$: +