QEEG Assessment

The QEEG (Quantitative Electroencephalography) is part of our initial assessment for all new clients. The results inform us which brain regions are over or under active, how well regions are communicating with each other, and how these patterns suggest or explain symptoms. When warranted, additional tests are included in the analysis, such as the Thatcher Brain Injury Index. These results form the basis of our customized neurofeedback protocols.

What to Expect

Our New Client Packet has instructions concerning how to prepare for your first QEEG: how to prep your hair/scalp; leaving jewelry/earrings at home; getting sufficient rest; whether to consume coffee; whether to discontinue meds for the recording.

To collect a QEEG, we first fit the client with an electro cap, which allows us to record from 19 different locations on the scalp. The electrodes are filled with a conductive gel and the contact point of each is worked such that the impendences are below 5 kOhm.

We record 10 minutes with eyes open and 10 minutes with eyes closed. It is important to sit very still during the process, yet to remain very relaxed. If there is considerable movement or muscle tension, we may need to record for a longer period. We will coach you so that we get the best data in the least amount of time. We will watch to see if you are exhibiting signs of sleepiness. We will ask for a list of the medications or supplements you may be taking, as these can have an impact on the data. The conductive gel will dry quickly afterwards and will easily wash out of your hair with warm water.

Analysis

It takes our clinicians several hours to analyze the data and prepare a report. The process is not automated. The reports typically range from 5 to 12 pages, depending on the complexity of the analysis. It also depends on whether it is a clinical report, for which there is more emphasis on a treatment plan; a forensic report, for which there is more emphasis on responding to questions made in a legal setting; or, a pre-post comparison for which we are looking at changes over time. After we have obtained a recording, our clinician inspects the raw data, marks sections that have too much artifact, and processes the rest against one or more clinical databases. These databases, which have been subject to the FDA regulatory process, compare the client’s brain data to that curated from a normative population across age and gender.

1. Raw data waveforms

We examine the raw EEG data for unusual features that suggest what patterns to look for in the other forms of analysis or whether we should refer the client to a neurologist.

2. FFT

The amount of power in each band may suggest that we assess for head injuries, trauma markers, global metabolic issues, cortical slowing, or thalamocortical desynchronization. In this image, each graph represents the amount of power (the vertical axis) at each frequency (horizontal axis) for each electrode location (front of the head is at the top in the image).

3. Surface Maps (Z-scores)

These maps are color coded to indicate the degree by which a client’s brain statistically differs from that of a “normal” brain. Areas that exhibit too little power in a particular band are colored on the blue side of the spectrum; areas with too much power are colored on the red side of the spectrum. We look for patterns such as whether the dysregulated patterns are diffuse (everywhere) or focal (isolated region); whether they are the same on the left and right sides (asymmetric); the location and how this relates to symptoms; the location and how this relates to the literature; and how the patterns relate to the FFT and LORETA views to indicate that we are seeing a consistent story told from multiple points of view.

4. LORETA (Z-Scores in 3D)

LORETA applies a mathematical function to the signals collected at each electrode site to identify the current source from which the signal on the surface has been generated. The results from this analysis compares favorably with fMRI scans. Our ability to pinpoint the location of a dysregulation, as well as the extent, allows us to select the most efficacious neurofeedback protocol. Also, by understanding the complete set of regions impacted, we can begin to understand the existing symptoms in terms of functional networks.

5. Theta/Beta Ratio

Some conditions, such as ADHD, are associated with an imbalance between the amount of power generated in each band. In particular, when we find that the ratio of the theta amplitude to the beta amplitude is greater than 2.0 in the front of the brain along the midline, then we suggest additional testing for attention issues.

6. Coherence and Phase

The similarity of signals generated between pairs of electrode sites inform us of the amount of information shared between regions. Complex brain functions, such as learning and memory, require coordination between regions. Decreased coherence results in reduced efficiency, longer processing times, and mistakes. Increased coherence leads to decreased flexibility, less cooperation with other brain regions, and stereotypic or stuck responses.

7. SCL

We import the Neuroguide processed data into a program called NFTools to generate a list of probable symptoms based on the dysregulated area. This is not to suggest that the client has all these symptoms, but rather that the dysregulated brain regions in the client match regions associated with these symptoms in the biomedical literature.

8. TBI Assessment

When there is a history of head injuries, if we find focal anomalies in the FFT or surface maps, or when concussion is called out in the symptom check list, we process the data through an add-on database that looks for cortical processing patterns common to a traumatic brain injury.

Sometimes, even when a brain injury has occurred, the damage has been done to the midbrain area or to vascular flow rather than to the neocortex. Unfortunately, an EEG will not reveal this kind of damage.

Additional Reading

Gluck, G. (2011). QEEG accepted in death penalty trial in Florida v. Nelson. Biofeedback39(2), 74-77.

Gunkelman, J. (2006). Transcend the DSM Using Phenotypes. Biofeedback34(3).

Johnstone, J., & Gunkelman, J. (2003). Use of databases in QEEG evaluation. Journal of Neurotherapy7(3-4), 31-52.

Johnstone, J., Gunkelman, J., & Lunt, J. (2005). Clinical database development: characterization of EEG phenotypes. Clinical EEG and Neuroscience36(2), 99-107.

Kaiser, D. A. (2007). What is quantitative EEG?. Journal of Neurotherapy10(4), 37-52.

Thatcher, R. W., & Lubar, J. F. (2009). History of the scientific standards of QEEG normative databases. Introd. Quant. EEG Neurofeedback2009, 29-59.

Thatcher, R. W. (1998). Normative EEG databases and EEG biofeedback. Journal of Neurotherapy2(4), 8-39.

Thatcher, R. W., Walker, R. A., Biver, C. J., North, D. N., & Curtin, R. (2003). Quantitative EEG normative databases: Validation and clinical correlation. Journal of Neurotherapy7(3-4), 87-121.

Thatcher, R. W., Moore, N., John, E. R., Duffy, F., Hughes, J. R., & Krieger, M. (1999). QEEG and traumatic brain injury: Rebuttal of the American Academy of Neurology 1997 report by the EEG and Clinical Neuroscience Society. Clinical Electroencephalography30(3), 94-98.

Tinius, T. (2004). Quantitative electroencephalographic analysis (QEEG) databases for neurotherapy: Description, validation, and application (Vol. 7, No. 3-4). CRC Press.