analysis:rhythms
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| ~~DISCUSSION~~ | ~~DISCUSSION~~ | ||
| - | Welcome! This is the home page for the optional hands-on (heads-on) 2016 edition of the "Rhythmic Brain" course. | + | Welcome! This is the home page for the optional hands-on (heads-on) 2020 edition of the "Rhythmic Brain" course. |
| - | ==== Contents ==== | + | ==== Getting started with Emotiv headsets ==== |
| - | === Installing MATLAB === | + | * Using the course laptops, launch the Emotiv Xavier Control Panel using the Desktop icon. |
| + | * Click "Continue without Cloud Services" | ||
| + | * Type an username ("TestUser" works fine) | ||
| + | * Follow the instructions on the screen. | ||
| - | Make sure you are on a Dartmouth network, such as Dartmouth Secure. Not KAF, Starbucks or eduroam. Then go [[https://caligari.dartmouth.edu/downloads/matlab/ | here]], click "Site License Downloads" and keep clicking the link at the bottom of the page until you get to the page for "R2014a": this is the version you want. Follow the instructions in the PDF. | ||
| - | When the installation is done, see if you can run MATLAB. You should be faced with a screen containing a bunch of windows, including one called "Command Prompt" containing ''>>''. | + | ==== Analysis modules ==== |
| - | If your MATLAB runs, great! Now decide if you'd rather: | + | * [[analysis:rhythms:step1|Module 1: Setting up, loading and plotting Emotiv data]] |
| - | - Familiarize yourself with MATLAB first. Go [[http://ctnsrv.uwaterloo.ca/vandermeerlab/doku.php?id=analysis:rhythms#getting_started_with_matlab | here]]. | + | * [[analysis:rhythms:step3|Module 2: Signal processing, basics of sampling (aliasing, Nyquist)]] |
| - | - Dive right in, working with Emotiv data. Read on. | + | * [[analysis:rhythms:step2|Module 3: Signal processing, Fourier analysis and power spectra]] |
| - | === Obtaining some sample Emotiv data === | + | (more advanced modules can be found [[analysis:nsb2018|here]].) |
| - | Click [[https://drive.google.com/file/d/0BzgbmK-cayRETEs2LVlwU0tHQzg/view?usp=sharing | here]]. Move this file to a location that shows you are careful with how you handle data. Something like ''\Documents\PSYC50\Data\TestData'' is a good choice, because it is clearly named and anticipates that you may have other data in the future beyond the file you grabbed just now. Something like ''\Downloads'' or ''\Desktop'' is bad because it'll get mixed in with lots of other files and maybe get deleted accidentally. | + | ==== Getting started with MATLAB ==== |
| - | + | ||
| - | Notice that this file has the extension ''.edf''. File extensions typically tell you about what **type** of file you are dealing with. In this case, our filetype is "EDF" (meaning it contains data arranged according the rules laid out in the [[http://www.edfplus.info/ | European Data Format specification]], which you can be happily ignorant about). All this means for you is that you need a file loader that can accept files of the EDF format, such as the one introduced in the next section. | + | |
| - | + | ||
| - | === Obtaining the code you need to load the Emotiv data === | + | |
| - | + | ||
| - | Pick one: the quick way, or the future-proof way. | + | |
| - | + | ||
| - | The quick way: download the MATLAB loader (from [[https://raw.githubusercontent.com/mvdm/PSYC50Rhythms/master/emotiv-loading/edfread.m | here]]), and place it in a sensible location like ''\Documents\PSYC50\Code\Loading''. This will work for now. However, if you ever want to access an updated version of this file, or other code that I put up for use in the course, you'll have to do more downloading. The future-proof way, below, addresses this. | + | |
| - | + | ||
| - | The future-proof way: I have added the code you'll need for loading EDF data, as well as for doing some other things, on a "GitHub repository". ''Git'' is a system for "distributed version control": put simply, a way to collaborate on a set of files that makes it easy to share and receive updates to those files. ''GitHub'' is a website that hosts those files in a convenient place so that the ''Git'' software can do its job. | + | |
| - | + | ||
| - | Let's make sure you have Git on your computer. If you are a Mac user, you should already have git installed. Open up a Terminal, and type ''git'' to verify that your computer recognizes it. If you are on Windows, I recommend you download the [[https://desktop.github.com/ | GitHub desktop]]. Then, open a "Git shell" (you should have an icon and start menu item) and type ''git'' to check this command is recognized. | + | |
| - | + | ||
| - | In your terminal/shell, navigate to a good place to put your %%GitHub%% code. | + | |
| - | + | ||
| - | === First steps: loading, plotting === | + | |
| - | + | ||
| - | + | ||
| - | === Getting started with MATLAB === | + | |
| Depending on your background and programming experience you might find the following resources helpful: | Depending on your background and programming experience you might find the following resources helpful: | ||
| - | * Textbook: {{:analysis:wallisch_matlabforneuro.pdf|Wallisch, MATLAB for Neuroscientists}} | + | * Textbook chapter: {{:analysis:wallisch_ch2.pdf|Wallisch, "MATLAB for Neuroscientists"}} |
| * [[http://www.mathworks.com/help/matlab/getting-started-with-matlab.html?s_cid=learn_doc|"Getting Started with MATLAB" Primer]]. | * [[http://www.mathworks.com/help/matlab/getting-started-with-matlab.html?s_cid=learn_doc|"Getting Started with MATLAB" Primer]]. | ||
| * [[http://www.mathworks.com/matlabcentral/about/cody/ | Cody]], a continually expanding set of problems with solutions to work through, with a points system to track your progress | * [[http://www.mathworks.com/matlabcentral/about/cody/ | Cody]], a continually expanding set of problems with solutions to work through, with a points system to track your progress | ||
| - | If you are unsure, take a look at the table of contents of the MATLAB Primer in the link above. If there are things you don't recognize, use the Primer itself, or Chapter 2 of the MATLAB for Neuroscientists book to get up to speed. If you've never used MATLAB, definitely start with the steps in the Primer. | + | If you are unsure, take a look at the table of contents of the MATLAB Primer in the link above. If there are things you don't recognize, use the Tutorials in the Primer itself, and/or Chapter 2 of the MATLAB for Neuroscientists book to get up to speed. If you've never used MATLAB, I recommend you start with the Tutorials in the Primer, and refer to the book chapter if you'd like a change of pace or a different way of explaining the same things. |
| Regardless of your MATLAB abilities, some great ways to keep learning are: | Regardless of your MATLAB abilities, some great ways to keep learning are: | ||
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| * [[http://stackoverflow.com/questions/tagged/matlab | MATLAB questions on StackOverflow]], a Q&A site where you can browse previous questions and add new ones | * [[http://stackoverflow.com/questions/tagged/matlab | MATLAB questions on StackOverflow]], a Q&A site where you can browse previous questions and add new ones | ||
| - | If you have no formal training in computer programming (i.e. you have never taken a "Intro to Computer Science" or "Introductory Programming" type course) a great introduction to the "Zen of programming" is to do the pen-and-paper exercises in this [[http://sites.tufts.edu/rodrego/files/2011/03/Secrets-of-Computer-Power-Revealed-2008.pdf | short chapter]] by Daniel Dennett ("The Secrets of Computer Power Revealed") before you embark on the MATLAB primer linked to above. Most people find it frustrating at first, but rewarding if they stick with it for a few hours. | + | If you have no training or experience in computer programming (i.e. you have never taken a "Intro to Computer Science" or "Introductory Programming" type course) a great introduction to the "Zen of programming" is to do the pen-and-paper exercises in this [[http://sites.tufts.edu/rodrego/files/2011/03/Secrets-of-Computer-Power-Revealed-2008.pdf | short chapter]] by Daniel Dennett ("The Secrets of Computer Power Revealed") before you embark on the MATLAB primer linked to above. Most people find it frustrating at first, but rewarding if they stick with it for a few hours. |
| - | === Resources === | + | ==== Resources ==== |
| - | The following textbooks provide more in-depth treatment of some of the topics we touch on in the course: | + | Emotiv documentation: |
| - | * Textbook: {{:analysis:leis_dspusingmatlab.pdf|Leis, Digital Signal Processing using MATLAB for Students and Researchers}} | + | * {{:analysis:quickstartguide2014.pdf|Quick Start Guide}} |
| - | * Textbook: {{:analysis:johnstonwu.pdf|Johnston and Wu, Foundations of Cellular Neurophysiology}} | + | * {{:analysis:emotiv_epoc_specifications_2014.pdf|Specification Sheet}} |
| - | * Textbook: {{:analysis:dayanabbott_theoneuro.pdf|Dayan & Abbott, Theoretical Neuroscience}} | + | * {{:analysis:epocusermanual2014.pdf|Manual}} |
| + | * {{:analysis:testbench_manual.pdf|TestBench (Emotiv software) manual}} | ||
| + | * [[https://www.emotiv.com/knowledge-base/electrode-oxidation/|Cleaning instructions]] | ||
| - | These are selected modules from a graduate course I teach on neural data analysis: | + | The following textbooks provide more in-depth treatment of some of the topics we touch on in the course: |
| - | * [[analysis:nsb2015:week0|Principles of (neural) data analysis]] | + | * Textbook: Leis, Digital Signal Processing using MATLAB for Students and Researchers |
| - | * [[analysis:course-w16:week1|Module 1: Setting up (MATLAB, paths, GitHub, accessing data; Week 1)]] | + | * Textbook: Johnston and Wu, Foundations of Cellular Neurophysiology |
| - | * [[analysis:course-w16:week2|Module 2: Introduction to neural data formats and preprocessing (Week 2)]] | + | * Textbook: Dayan & Abbott, Theoretical Neuroscience |
| - | * [[analysis:course-w16:week3long|Module 3: Visualizing raw neural data in MATLAB (Week 3)]] | + | |
| - | * [[analysis:course-w16:week4|Module 4: Anatomy of time series data, sampling theory (Week 4)]] | + | |
| - | * [[analysis:course-w16:week5|Module 5: Fourier series, transforms, power spectra (Week 4)]] | + | |
| - | * [[analysis:course-w16:week6|Module 6: Filtering: filter design, use, caveats (Week 5)]] | + | |
| - | * [[analysis:course-w16:week7|Module 7: Time-frequency analysis: spectrograms (Week 5)]] | + | |
| - | * [[analysis:course-w16:week11|Module 11: Interactions between multiple signals: coherence, Granger causality, and phase-slope index (Week 8)]] | + | |
| - | * [[analysis:course-w16:week12|Module 12: Time-frequency analysis II: cross-frequency coupling (Week 9)]] | + | |
| - | * [[analysis:course-w16:week13|Module 13: Spike-field relationships: spike-triggered average, phase locking, phase precession (Week 10)]] | + | |
| - | === Note for Linux users === | + | Some tasks to try to generate specific brain rhythms: |
| - | The lab codebase is set up for machines running 64-bit Windows 7 or Mac %%OS%% X. If you want to use Linux or some other %%OS%% you will probably need to compile some of the low-level loading functions yourself. Some pointers for this are provided in subsequent modules when loading is introduced. | + | * Occipital-parietal alpha (10 Hz): eyes-open vs eyes-closed contrast |
| + | * Frontal alpha: negative high arousal picture viewing, or mentally counting backwards from a large number in steps of 7 or 13 | ||
| + | * Drawing a stick figure (see for yourself what happens, take care to avoid moving artifacts) | ||
analysis/rhythms.1459274287.txt.gz · Last modified: 2018/07/07 10:19 (external edit)
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