## What is Z-Score Training?

The Z-Score training software was originally developed in close co-operation between Brainmaster and Applied Neurosciences. It enables training EEG data in real time by using a FDA approved database, which contains EEG test results of more than 600 healthy adults and children.

**The relevant characteristics of the Z-score procedure:**

- The trainees receive feedback when the EEG data recorded during the neurofeedback training falls within a certain range of plus/minus 1,0 Z-score (standard deviations).
- Z-Score training measures the data at the individual recording points, as well as the connections between these sides. It calculates parameters like amplitude (power), asymmetry, relation, phase and coherency within the frequencies from delta to Hibeta.
- For beginners in the field of neurofeedback the Z-Score training is actually much simpler to learn, even though the program implements very complex operations.
- Z-Score training can manage a big number of processes at the same time by constantly informing the brain about its condition and executing hundreds of calculations every second. It rids the user of constantly having to make decisions and adjustments. The dynamic Z-score protocols even adapt intelligently to the respective requirements. The trainer can dedicate himself completely to the monitoring the client and optimizing the treatments process during training.
- Z-Score-training is substantially simpler to learn than the traditional neurofeedback protocol selection methods. Longtime practitioners learning the Z-score-training also have the option to incorporate some traditional protocols as required.
- One can use the Z-scores as a sort of GPS. Regardless of what frequency is being trained- the display of Z-scores always shows whether the changes are developing in a favorable or unfavorable direction.

**Explanation of the graph**

The curve shows the so-called normal or Gaussian distribution. Just like, for instance, body size or the intelligence quotient in humans exhibit normal distribution, EEG waves also exhibit normal distribution, which is indicated by so called standard deviations. The question of interest here is; at what point does a measured value actually become deviant.

Firstly, the average value is important: Simply said, it is the value calculated by summarizing all measurement results and dividing the sum with the number of measured individuals. This value is at the middle of the curve; statistically speaking this value has 0 deviations from the norm, because it is the absolute average value.

**AVERAGE SD (standard deviation)**

To obtain a simple measure for the deviation, the Z-score scale has only 3 numerical figures left and right from zero. The numbers to the left of the zero receive the minus sign; the numbers right from the zero the plus sign. Simply put: left of zero means too little, right of zero means too much.

The largest deviation 3 is furthest from 0 and at the same time it signifies that only very few people possess such a deviation from the norm. Instead of comparing a variety of numbers, with the Z-score scale one gains a fast and clear overview: the larger the distance to zero, the higher the probability of correlation with symptoms. Naturally, not every deviating value is equal to a symptom. The distinction is made with the help of the exact anamnesis and questioning of the client. Here it concerns the range of a population with normal values. E.g. an IQ within the range of 85 - 115 is considered normal. Hereby the Z-score of -1 would be at 85 and the Z-score of +1 would be at an IQ of 115. A Z-score of 1 corresponds therefor with the term of the standard deviation. It is the measure, which through statistical/mathematical computations calculates the exact standard measure. Therefore, the value 1 corresponds to the standard deviation and thus a limit value. Research has shown that the correlation to symptoms usually appears beyond the standard deviation.

The curve is highest between -1 and +1, since most adult individuals (68%) are normal. It is exactly the same with Z-scores. Most individuals possess normal EEG data. Deviations are marked as such only if the measured values do not lie between -1 and +1.

Z-Score Training has shown to be very effective in many disorders. We taach this approach in our workshops.

Here is collection of research papers:

Thatcher, R.W. EEG database guided neurotherapy. In: J.R. Evans and A. Abarbanel Editors, Introduction to Quantitative EEG and Neurofeedback, Academic Press, San Diego, 1999.

Thatcher, R.W. QEEG and traumatic brain injury: Present and future. Brain Injury, 12: 13-21, 1999.

Thatcher, R.W. “Handbook of QEEG and EEG Biofeedback”, Ani Publishing, Co., 2012

Collura, T., Guan, J., Tarrent, J., Bailey, J., & Starr, R. (2010). EEG biofeedback case studies using live z-score training and a normative database. Journal of Neurotherapy, 14(1), 22–46.

Collura, T., Thatcher, R., Smith, M. L., Lambos, W., & Stark, C. (2009). EEG biofeedback training using live z-scores and a normative database. Philadelphia : Elsevier.

Collura, T. (2008). Whole head normalization using live Z-scores for connectivity training. Neuroconnections, April 2008, p 12-18.

Collura, T. (2008). Time EEG Z-score training: Realities and prospects. In: Evans, J., Arbanel, L. and Budsynsky, T. Quantitative EEG and Neurofeedback, Academic Press, San Diego , CA .

Decker, S.L. Roberts,A.M. and Green, J.J. (2014). LORETA Neurofeedback in College Students with ADHD. . In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Foster, D.S. and Thatcher, R.W. (2014). Surface and LORETA Neurofeedback in the Treatment of Post-Traumatic Stress Disorder and Mild Traumatic Brain Injury. . In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Hammer, B.U., Colbert, A.P., Brown, K.A. and Ilioi, E. C. (2011). Neurofeedback for Insomnia: A Pilot Study of Z-Score SMR and Individualized Protocols. Appl Psychophysiol Biofeedback, DOI 10.1007/s10484-011-9165-y

Koberda, J.L. (2011). Clinical advtabges of quantitative electroencephalogram (QEEG) application in general neurology ractice. Neuroscience Letters, 500(Suppl.), e32.

Koberda, J.L, Moses, A., Koberda, L. and Koberda, P. (2012). Cognitive enhancement using 19-Electrode Z-score neurofeedback. J. of Neurotherapy, 16(3): 224-230.

Koberda, J.L, Hiller, D.S., Jones, B., Moses, A., and Koberda, L. (2012). Application of Neurofeedback in general neurology practice. J. of Neurotherapy, 16(3): 231-234.

Koberda, J.L. (2014). Neuromodulation-An Emerging Therapeutic Modality

in Neurology. J Neurol Stroke 2014, 1(4): 00027

Koberda J, L. and Stodolska-Koberda U (2014). Z-score LORETA Neurofeedback as a Potential Rehabilitation Modality in Patients with CVA. J Neurol Stroke 1(5): 00029.

Koberda, J.L. et al. 2012. Cognitive enhancement using 19-electrode Z-score Neurofeedback. J. Neurotherapy 3.

Koberda J.L. (2012). Autistic Spectrum Disorder (ASD) as a Potential Target of Z-score LORETA Neurofeedback. The Neuroconnection- winter 2012, edition (ISNR), p. 24.

Koberda JL, Koberda P, Bienkiewicz A, Moses A, Koberda L. Pain Management Using 19-Electrode Z-Score LORETA Neurofeedback. Journal of Neurotherapy, 2013, 17:3, 179-190.

Koberda JL, Moses A, KoberdaP, Winslow J. Cognitive Enhancement with LORETA Z-score Neurofeedback. AAPB meeting, 2014.

Koberda,J.L. (2012). Comparison of the effectiveness of Z-score Surface/LORETA 19-electrode Neurofeedback to standard 1-electrode Neurofeedback- J. Neurotherapy.

Koberda, J.L. (2014). Therapy of Seizures and Epilepsy with Z-score LORETA Neurofeedback. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Koberda JL, Koberda L, Koberda P, Moses A, Bienkiewicz A (2013) Alzheimer’s dementia as a potential target of Z-score LORETA 19-electrode Neurofeedback. In: Neuroconnection. Winter edition, p.30-32.

Koberda, J.L. (2014). Z-score LORETA Neurofeedback as a Potential Therapy in Depression/Anxiety and Cognitive Dysfunction. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Lambos, W.A. and Williams, R.A. (2014). Treating Executive Functioning Disorders Using LORETA Z-scored EEG Biofeedback. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Lambos, W.A. and Williams, R. A (2014). Treating Anxiety Disorders Using Z-scored EEG Biofeedback. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Little, R.M., Bendixsen, B. H. and Abbey, R.D. (2014). 19 Channel Z-Score Training for Learning Disorders and Executive Functioning. . In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Lubar, J.L. (2014). Optimal procedures in Z score neurofeedback: Strategies for maximizing learning for surface and LORETA Neurofeedback. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Smith, M.L. (2008). Case study: Jack. Neurosconnections, April, 2008.

Stark, C.R. (2008). Consistent dynamic Z-score patterns observed during Z-score training sessions – Robust among several clients and through time for each client. Neuroconnections, April, 2008.

Thompson, M., Thompson, L. and Reid-Chung, A. (2014). Combining LORETA Z-Score Neurofeedback with Heart Rate Variability Training. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Thompson, M., Thompson, L., & Reid, A. (2010). Functional Neuroanatomy and the Rationale for Using EEG Biofeedback for Clients with Asperger’s Syndrome. Journal of Applied Psychophysiology and Biofeedback, 35(1), 39-61.

Thatcher, R.W. (2000). 3-Dimensional EEG Biofeedback using LORETA., Society for Neuronal Regulation, Minneapolis, MN, September 23, 2000.

Thatcher, R.W. (2010). LORETA Z Score Biofeedback. Neuroconnections, December, pg. 14-17.

Thatcher, R.W. (2013): Latest Developments in Live Z-Score Training: Symptom Check List, Phase Reset, and Loreta Z-Score Biofeedback, Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 17:1, 69-87

Thatcher, R.W. (2010). LORETA Z Score Biofeedback. Neuroconnections, December, p. 9 – 13.

Thatcher, R.W. (2012). Handbook of Quantitative Electroencephalography and EEG Biofeedback. Anipublishing, Inc., St. Petersburg, Fl

Thatcher, R.W. (2013). Latest Developments in Live Z-Score Training: Symptom Check List, Phase Reset, and Loreta Z-Score Biofeedback. Version of record first published: 26 Feb. J. of Neurotherapy.

Thatcher, R.W. North, D.M.and Biver, C.J. (2014). Technical foundations of Z score neurofeedback. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Thatcher, R.W. North, D.M. and. Biver, C.J. (2014). Network Connectivity and LORETA Z score NFB. In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Thatcher, R.W. North, D.M. and Biver, C.J. (2014). BrainSurfer 3-Dimensional Z Score Brain-Computer-Interface. . In: RW Thatcher and JF Lubar “Z Score Neurofeedback: Clinical Applications”. Academic Press, San Diego, CA ( 2014).

Wigton, N.L. (2013) Clinical Perspectives of 19-Channel Z-Score Neurofeedback: Benefits and Limitations, Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 17:4, 259-264.