
Explore EEG phenotypes as semi-stable electrophysiological states, with recognizable categories and heritable patterns, and learn how neurofeedback can modify these patterns through mini map analysis of frequency bands.
Explore epileptiform and paroxysmal EEG as markers of brain instability, identify spikes with base under 60 ms plus slow waves, and review motor-strip neurofeedback protocols.
Focal slow activity or focal abnormalities appear as localized theta or delta waves, not epileptiform, often episodic, with neurofeedback training targeting the affected area using 12–15 Hz rewards.
Frontal lobe hypoperfusion presents as frontal alpha or theta excess, indicating reduced blood flow and symptoms like poor focus and memory; neurofeedback targets frontal sites with specific reward/inhibition frequencies.
Analyze frontal asymmetries between F3/F4 (and F7/F8), noting left slow/right fast patterns linked to emotional dysregulation and depression, with neurofeedback targets for alpha, theta, and beta.
Explore excess temporal lobe alpha with episodic sharp transients, its bilateral or focal patterns, artifact considerations, and implications for language, memory, social perception, and basal artery issues, with neurofeedback targets.
Identify the faster alpha variant by a posterior dominant rhythm at parietal sites with eyes closed, 0.5 Hz above age norms and tuned to 8-10 Hz, differentiating from beta.
Identify the slower alpha variant in the posterior dominant rhythm, with alpha about 0.5 Hz slower than age expectations and theta overlap in younger children, differentiating from diffuse slowing.
Identify spindling excess beta as beta above 22 Hz and 20 microvolts, from muscle activity and fast alpha. Neurofeedback aims to suppress beta peak to stabilize anxiety and bipolar disorder.
Analyze persistent eyes open alpha, where alpha does not attenuate by 50% on eye opening and may exceed 8–12 Hz, linking to default mode network and PTSD; outline neurofeedback targets.
Identify cingulate dysfunctions by midline alpha, theta, beta, or delta excess at Fz or Cz. Use targeted neurofeedback rewards at Fz, F3, F4, and PZ to address OCD and symptoms.
Identify persistent mu rhythm, an alpha-range pattern at C3/C4 not attenuated by movement, and note its link to the mirror neuron system and potential language or social perception disorders.
Correlate the EEG phenotypes with the client’s presenting symptoms to guide neurofeedback training, seeking overlap between EEG patterns and symptoms, using multiple assessment tools and working hypotheses.
Develop neurofeedback protocols for individuals with multiple EEG phenotypes by balancing inhibition at five hertz with reward frequencies eight to eleven hertz and twelve to fifteen hertz to improve stability.
Complete the review questions to earn a certificate of completion, BCIA recertification credit, and continuing education credits, with retakes available; treat course as reference material for EEG and neurofeedback practice.
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 2 of the Raw EEG 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.