The goal of this course is to learn EEG from scratch, with a focus on two popular analysis techniques in psycholinguistics: ERPs and timefrequency decomposition. When the course is finished, you should be able to collect, process, plot, and statistically analyze EEG data.
Here is a syllabus for Fall 2020.
There are two textbooks out there for EEG in cognitive science that are absolutely terrific. Honestly, these two textbooks together provide a complete introduction to the two main analysis techniques in the literature: ERPs and timefrequency decomposition. You could just read these carefully, and be ready to do your own EEG research. The point of this course is to supplement the material in those textbooks to help really solidify the content... and to give you hands on practice doing the analyses.
book  notes 
Steve Luck: An introduction to the eventrelated potential technique  This is the book to start with. It provides a gentle introduction to EEG, and a complete introduction to the ERP technique. 
Mike X Cohen: Analyzing neural time series data  This the book to buy if you are interested in timefrequency decomposition. It assumes some familiarity with EEG and ERPs. 
EEG analysis requires a framework for scientific computing. In principle, you can choose from Matlab, Python, and R (see the slides for more information). In practice, Matlab has the most welldeveloped tools for EEG analysis. Therefore, for this course, we will use Matlab.
Here are the pieces of software you will need to install on your computer, and in some cases, supplementary material to help you learn more about the software.
software  purpose 
MATLAB (free for UConn students)  This is the language we will use. After installation, you will need to update it (currently update 4) to run EEGLAB. 
Mike X Cohen: Matlab for Brain and Cognitive Scientists  Cohen wrote a textbook for Matlab. It is a great companion to his timefrequency book. 
EEGLAB (free)  This is a MATLAB toolbox for EEG analysis. It requires you to update Matlab 2018a to update 4. 
ERPLAB (free)  This is a plugin for EEGLAB written by Steve Luck to make ERP analysis easier. It will be our primary tool for ERP analysis. 
BVA import (free)  This is a plugin for EEGLAB to import data in the format that our Brain Products EEG system uses. 
Mass Univariate Toolbox (free)  This is a MATLAB toolbox for statistical analysis of ERP data. We will use it with ERPLAB. 
Factorial Mass Univariate Toolbox (free)  This extends the mass univariate toolbox to factorial designs. We probably won't use it, but it may be useful. 
FieldTrip (free)  This is another MATLAB toolbox for EEG analysis. We will use it for timefrequency decomposition. It also does statistics for timefrequency results. 
Modified FieldTrip function  Fieldtrip knows that Brain Products triggers have S's and R's in them. I remove the S's and R's, so I modified one of the fieldtrip functions. You need to place it in this folder inside your Matlab installation: \fieldtrip\trialfun. 
RStudio (free)  R is a free language for statistical analysis and plotting. We will use it to make pretty plots. RStudio is a graphical user interface that makes using R easier. 
Garrett Grolemund and Hadley Wickham: R for Data Science  This is a free book written by the creators of RStudio to explain a number of packages (the tidyverse) that they have created to make data analysis easier in R. 
We will use a real (sentence final) N400 experiment for our sample data. There are 36 participants in this data set. Each participant has three files (BP's standard format). I've created three zipped folders, each containing 12 participants. Each folder is about 3GB.
participants 112  participants 1324  participants 2536  Description of the experiment 
I created a set of slides to accompany the ERP part of this course up through the section on mass univariate permutation tests. Here is the full set: keynote version  pdf version [last updated 09.14.20]. I will reference the relevant sections in the schedule below.
There are also two standalone sets of slides (one on ANOVAs/LMEMs, and one on bits of math for timefrequency decomposition). I've linked those directly in the relevant section of the schedule below.
topic  slides  readings  materials  
Introduction  section 1  Luck 1
Luck 2 

Fundamentals  section 2  Luck 2
Jackson and Bolger 2014 
An interactive tutorial on sine waves and Fourier transform
A second interactive tutorial 

Handson training  Luck 5  
The ERP processing pipeline in ERPLAB  section 4 (19)  Luck 6
Luck 9 
an example script for ERPLAB  
Plotting ERPs  section 4 (10)  script to export ERPs
script for waveforms script for topoplots exported ERPs example plots 

Measurements of ERPs  section 4 (11)  Luck 9
Luck 10 

Multiple comparisons and mass univariate permutation tests  section 4 (12)  Groppe et al. 2011  R script to demonstrate the multiple comparisons problem
example data 

Traditional ANOVAs and LMEMs  keynote or pdf
(these are not part of the full set above) 
list of ERPs for measurements
exported measurements R script for ANOVAs 

The ERP processing pipeline in FieldTrip  preprocessing script
ERP script 

The dotproduct and convolution  keynote or pdf
(these are not part of the full set above) 
Cohen 10  A youtube series on linear algebra
an applet demonstrating the dot product another explanation of the dot product a real world example of convolution an applet demonstrating convolution 

Complex waves and Morlet wavelets  keynote or pdf
(these are not part of the full set above) 
Cohen 12
Cohen 13 

Timefrequency in Fieldtrip  preprocessing script (identical to the one above, but without lowpass filtering)
Morlet wavelet script example TF data (to demonstrate plotting and stats) 

Designing EEG experiments  Luck 4
Duncan et al. 2009 

The probabilistic prediction (word preactivation) debate  Delong et al. 2005
9 labs replication 2018 Delong et al. 2017 Ito et al. 2017a Ito et al. 2017b Yan et al. 2018 
I've done my best to create a comprehensive introduction to EEG on this page. But I am just one person with my own limited experiences. There are lots of resources out there. Here I list a few that I have found myself consulting recently.
link  notes 
ERPinfo.org Resources  The Luck lab has created a number of resources for learning EEG, including an intro course, a sample data set, and a bootcamp course. 
MNE Python tutorials  MNE Python is another software solution for analyzing M/EEG data (with an original focus on source localization). I do not cover it in this course (yet), but there are a number of tutorials online. 
Comparing toolkits  The fieldtrip group organized a workshop comparing three toolkits: fieldtrip, MNE python, and brainstorm. 
Advanced fieldtrip  The fieldtrip group organizes a number of workshops each year. This one focused on advanced analysis techniques in fieldtrip. 
Python neurobootcamp  OHSU organized a workshop for neuroscientists who want to use Python in their analysis pipeline (for behavioral, electrophysiological, and imaging studies). 