• Module 1: Setting up, loading and plotting Emotiv data
• Module 2: Signal processing, basics of sampling (aliasing, Nyquist)
• Module 3: Signal processing, Fourier analysis and power spectra

(more will be added as we go along)

Depending on your background and programming experience you might find the following resources helpful:

• Textbook: Wallisch, MATLAB for Neuroscientists
• "Getting Started with MATLAB" Primer.
• 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.

Regardless of your MATLAB abilities, some great ways to keep learning are:

• Mathworks staff blogs, especially “Loren on the Art of MATLAB” is a treasure trove of tips and tricks
• 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 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.

Emotiv documentation:

• Quick Start Guide
• Specification Sheet
• Manual
• TestBench (Emotiv software) manual
• Cleaning instructions

The following textbooks provide more in-depth treatment of some of the topics we touch on in the course:

• Textbook: Leis, Digital Signal Processing using MATLAB for Students and Researchers
• Textbook: Johnston and Wu, Foundations of Cellular Neurophysiology
• Textbook: Dayan & Abbott, Theoretical Neuroscience

These are selected modules from a graduate course I teach on neural data analysis:

• Principles of (neural) data analysis
• Module 1: Setting up (MATLAB, paths, GitHub, accessing data)
• Module 2: Introduction to neural data formats and preprocessing
• Module 3: Visualizing raw neural data in MATLAB
• Module 4: Anatomy of time series data, sampling theory
• Module 5: Fourier series, transforms, power spectra
• Module 6: Filtering: filter design, use, caveats
• Module 7: Time-frequency analysis: spectrograms
• Module 11: Interactions between multiple signals: coherence, Granger causality, and phase-slope index
• Module 12: Time-frequency analysis II: cross-frequency coupling
• Module 13: Spike-field relationships: spike-triggered average, phase locking, phase precession

The tutorials provided here are 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.