Principles and Statistics of Individualized Live and Static Z-Scores

  • Thomas Collura
  • Jeffrey Tarrant, Ph.D. Neuromeditation Institute
Keywords: eeg, qeeg, statistics, database, z-scores


This report describes and briefly characterizes a method for computing quantitative EEG (qEEG) z-scores based on a modification of the typical methods used for qEEG reporting. In particular, it describes using a sample of EEG from a single individual, and creating a reference database from the individual sample, in contrast to using
a population of individuals as the source data. The goal of this method is to quantify and localize within-subject changes that may arise due to time or various factors. We refer to this approach as “z-builder,” because the z-score reference is constructed or “built” on a per-subject basis in the office or laboratory and is not derived from a reference obtained from an outside source. It is confirmed that z-scores for EEG acquired during a test period can be calculated based on a single previously recorded reference sample from an individual, and that the resulting z-scores obey the expected statistical distribution. Reference data can be calculated using samples in the 1- to 5-minute range, and subsequent static or dynamic z-scores for a test sample can then be computed using this reference data in lieu of a population database. It is confirmed that, in the absence of systematic change in the EEG, z-scores generally fall well within the range of ±1.0, providing a sensitive indicator when
changes do occur. It is shown that this method has value in assessing individual stability of EEG parameters and for quantifying changes that may occur due to time effects, aging, disorders, medications, or interventions.

Author Biography

Jeffrey Tarrant, Ph.D., Neuromeditation Institute

Dr. Jeff Tarrant is a licensed psychologist and board certified in neurofeedback. He is the founder and CEO of the NeuroMeditation Institute (NMI), LLC and provides certification training for NMI therapist and instructors. He is the author of the book, “Meditation Interventions to Rewire the Brain.” Dr. Tarrant is the Chief Science Officer for StoryUp VR where he is involved in research and development of therapeutic virtual reality programs. Dr. Tarrant’s research focuses on exploring brainwave changes that occur as a result of contemplative practices, virtual reality, and entheogenic medicines.

Dr. Tarrant maintains a private practice in Eugene, OR where he combines modern and ancient technologies to form an integrated and holistic system of health and healing. Following an initial assessment, Dr. Tarrant works with the client to develop a treatment plan using one or more modalities including EEG NeuroMeditationAudio Visual EntrainmentPhotobiomodulationNon-Ordinary State Psychotherapy (NOSP), and VibroAcoustic Therapy (VAT). Dr. Tarrant specializes in working with non-ordinary states to facilitate deep insight and psychological healing. He has been practicing meditation and Qigong for over 20 years.


Collura, T. F. (2014). Technical Foundations of Neurofeedback. New York, NY: Routledge.

Collura, T. F. (2014, Spring). Specifying and developing references for live z-score neurofeedback. NeuroConnections, 26–39. Retrieved from

Collura, T. F. (1990). Real-time filtering for the estimation of steady-state visual evoked potentials. IEEE Transactions on Biomedical Engineering, 37(6), 650–652. Accessed from

Collura, T. F., Thatcher, R. W., Smith, M. L., Lambos, W. A., & C. R. Stark (2009). EEG Biofeedback training using live Z-scores and a normative database. In J. R. Evans, T. H. Budzynski, H. K. Budzynski., & A. Arbanal (Eds.), Introduction to quantitative EEG and neurofeedback: Advanced theory and applications, Second Edition (pp. 103–142). New York, NY: Elsevier.

Host-Mandel, A., & Handel, P. (2000). Effects of sampling and quantization on single-tone frequency estimation. IEEE Transactions on Signal Processing, 48(3), 650–662. Accessed from

Kerson, C., deBeus, R., Lightstone, H., Arnold, L. E., Barterian, J., Pan, X., & Monastra, V. (2020). EEG theta/beta ratio calculations differ between various EEG neurofeedback and assessment software packages: Clinical interpretation. Clinical EEG and Neuroscience, 51(2), 114–120.

Messick, S. (1998). Test validity: A matter of consequence. Social Indicators Research, 45(1–3), 35–44.

Miller, J. N., & Miller, J. C. (2018). Statistics of repeated measures. In J. N. Miller & J. C. Miller (Eds.), Statistics and chemometrics for analytical chemistry (6th ed., pp. 17–26). Harlow, England: Pearson. Retrieved from

Siever, D., & Collura, T. (2017). Chapter3 – Audio-visual entrainment: Physiological mechanisms and clinical outcomes. In J. R. Evans & R. P. Turner (Eds.), Rhythmic stimulation in neuromodulation (pp.51–95). New York, NY: Elsevier.

Social Science Statistics (2019). T Test Calculator for 2 Dependent Means. Retrieved from

Thatcher, R. W. (2008, April). Z-Score EEG biofeedback conceptual foundations. NeuroConnections, 9–11. Retrieved from

Technical Notes