Analysing Raw EEG - Part 1

This course is made up of two parts that are sold separately - they are not standalone - together they make the one course as described here.

While 19-channel QEEG analysis is seen as the gold standard, world-leading experts like Jay Gunkelman assert that you must be able to see evidence in the raw EEG of any conclusion you make.

But learning to analyse raw EEG can seem like a daunting mountain to climb, where do you start? And how can you be sure you are going in the right direction?

Dr. Moshe Perl has been teaching and mentoring practitioners for decades on how to accurately analyse EEG. His expertise is based on years of study and mentorship with Jay Gunkelman and other QEEG experts, seeing thousands of clients at his neurofeedback clinic and sharing knowledge with his network of colleagues and mentees.

This course provides you with the clarity needed to make the complexities of EEG analysis accessible and easily understandable.

2.5 hours of BCIA recertification (professional development) credit are available upon completion of both the two parts of this course.

In 2005 Jack Johnstone, Jay Gunkelman, and Joy Lunt published a paper on EEG phenotypes.

Phenotypes are a way of understanding the failure modes in the EEG, that is, the variety of ways the EEG “messes up” and produces emotional, cognitive and physiological symptoms. The beauty of the phenotypes is that there are only a limited number of them, all of them visible in the raw EEG. That makes them fundamental and basic. Know your phenotypes and you will know your EEG. And conversely, as Jay Gunkelman says, “if you can’t see it in the raw EEG, it doesn’t exist.”

This course teaches you how to identify 15 phenotypes to give you a strong foundation in EEG analysis, particularly to inform neurofeedback protocol selection.

Some additional and important topics are also covered:

• Artifacts - items in the EEG record that are not generated by the brain (e.g. eye blinks) - how to identify and deal with them

• Sleep and drowsiness - very impactful on the EEG, which can lead to poor analysis if not addressed properly

• How to deal with several phenotypes in the one person

This is not a "basics" or "101" course in EEG. It will not introduce "the basics of EEG", nor the technical side of taking or reviewing measurements (i.e. how to use equipment) as it is assumed that students already possess a basic understanding of EEG analysis and have access to equipment that allows them to measure and analyse raw EEG.

Ideally students will have already completed a basic hands-on course in neurofeedback and/or a BCIA certified course in neurofeedback.

In Part 1 of the Raw EEG course we look at some of the things that may cloud our ability to understand and interpret correctly what we are seeing.  Chief among them are artifacts.  Sometimes we are able to look beyond the artifacts or even use the presence of artifacts to help us understand what issues the client might have.  At other times we want to remove the artifacts from the record so we can do numerical analyses on the data.  We discuss some of the most common ways to remove artifacts, the pros and cons.  Another critical issue that arises when recording EEG is the level of wakefulness of the client.  Our analysis of EEG is based on the assumption that our client is fully awake.  In reality, our clients might easily be drowsy or may actually have a sleep issue or disturbance.  EEG changes dramatically when we become drowsy or fall asleep and we need to be alert to that possibility when analysing their EEG.  In Part 2 of this course we give an in-depth review of patterns found in the EEG, how to identify them, what behaviours or emotional symptoms are associated with them and some possible starting points for neurofeedback training. 

This Raw EEG Analysis course is a stand-alone course. For those who are interested in looking at comparisons of averaged EEG data values rather than purely looking at EEG morphology, our 'Learn to read the EEG' course, an in-depth review of the significance of variations in averaged EEG values over the cortex, can provide a great deal of useful information as well.