analysis:rhythms:step3
====== Differences ====== This shows you the differences between two versions of the page.
| Next revision | Previous revision | ||
|
analysis:rhythms:step3 [2016/03/29 19:39] mvdm created |
analysis:rhythms:step3 [2018/04/17 15:20] (current) |
||
|---|---|---|---|
| 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 $y = sin(2 \pi f t)$: | + | |
| <code matlab> | <code matlab> | ||
analysis/rhythms/step3.1459280371.txt.gz · Last modified: 2018/04/17 15:22 (external edit)
Page Tools
