Z Score Neurofeedback

Clinical Applications

Robert W. Thatcher, Joel F. Lubar

• Publisher: Academic Press
• Year: 2014
• Print ISBN: 9780128012918
• eBook ISBN: 9780128014646
• Topic: Medicine
• Subtopic: Neurology

Neurofeedback is utilized by over 10, 000 clinicians worldwide with new techniques and uses being found regularly. Z Score Neurofeedback is a new technique using a normative database to identify and target a specific individual's area of dysregulation allowing for faster and more effective treatment. The book describes how to perform z Score Neurofeedback, as well as research indicating its effectiveness for a variety of disorders including pain, depression, anxiety, substance abuse, PTSD, ADHD, TBI, headache, frontal lobe disorders, or for cognitive enhancement. Suitable for clinicians as well as researchers this book is a one stop shop for those looking to understand and use this new technique.- Contains protocols to implement Z score neurofeedback- Reviews research on disorders for which this is effective treatment- Describes advanced techniques and applications

Chapter 1

History and Technical Foundations of Z Score EEG Biofeedback

Robert W. Thatcher, Carl J. Biver and Duane M. North, NeuroImaging Laboratory, Applied Neuroscience, Inc., Seminole, FL, USA

The history and technical foundations of Z score electroencephalogram (EEG) biofeedback, including LORETA Z score biofeedback, is reviewed. The statistical standards are discussed and the step-by-step conceptual foundations of Z score biofeedback are explained. The central concept is linking symptoms to dysregulated nodes and connections between nodes in networks in the brain. The goal is to reinforce increased stability and efficiency in neural networks by reinforcing toward the center of a normal reference population. The use of Z scores for real-time or “live” biofeedback unifies different EEG metrics (e.g., power, amplitude, coherence, phase) to a single metric, i.e., the metric of the Z score with a mean=0 and a standard deviation=1 in the ideal case. Z score biofeedback also simplifies the EEG biofeedback process by providing clinicians with a “guide” or reference to determine threshold setting for biofeedback. For example, with raw score biofeedback, clinicians must guess at a threshold setting to trigger biofeedback. With Z score biofeedback, the guess work is removed since all metrics are treated the same in which the direction of biofeedback is toward Z=0.

Keywords

LORETA Z scores; real-time Z score biofeedback; symptom checklist

Introduction

The statistical foundations of Z score electroencephalogram (EEG) biofeedback are based on the fact that after the appropriate transform, all EEG variables are Gaussian distributed with sample sizes equal to or greater than about 20 subjects per age group. A review of the statistical foundations is in the paper “History of the Scientific Standards of QEEG Normative Databases” (Thatcher & Lubar, 2008). The Z score is a statistic of distance from the mean adjusted for variance or Z=mean–measure divided by the standard deviation where at infinity with a random sampling the ideal Gaussian mean=0 and the standard deviation=1. This is a powerful statistic that also has a multivariate expression called the Mahalanobis distance (Cooley & Lohnes, 1971). The interpretation of a Z score depends on the assumptions of (1) a Gaussian distribution (>0.95) after transform of the raw digital data, (2) a N>20 of a representative sample of the measure of interest, (3) the measures to compute the mean must be the same variables used to compute a Z score (e.g., FFT means cannot be used to compute joint time–frequency analysis (JTFA) Z scores or vice versa). The reason there is a violation of statistical sampling theory if one uses the FFT to compute a JTFA Z scores is because the FFT multiplies sine/cosine waves over a discrete interval of time (e.g., 1–2 s) as well as windowing to approximate infinity, whereas JTFA methods like Wavelets or Complex Demodulation do not use windowing or the convolution of sine waves over a discrete window of time. Although the FFT and digital filters can converge at infinity, the fact is that the same group of subjects produce different means when comparing the FFT to JTFA and therefore, standard #3 is violated. It is easy to show that the FFT produces different means than the JTFA on the same data samples, e.g., we find over 14% differences and Brainmaster 8% difference in means between the FFT and filter means which is a significant error and is in violation of statistical sampling theory.

The remainder of this chapter will review and integrate the statistical and technical methods used and described in various forums. In the sections to follow, steps are taken to adhere to the basic statistical foundations in which there is no violation of statistical sampling theory because only complex demodulation means are used to compute complex demodulation Z scores in all Neuroguide analyses of Z score biofeedback measures presented in this chapter.

First Use of Gaussian Probabilities to Identify “Dysregulation” in the Brain

The fundamental design concepts of Z score biofeedback were first introduced by Thatcher (1998, 1999, 2000a,b,c). The central idea of the instantaneous Z score is the application of the mathematical Gaussian curve or “bell-shaped” curve by which probabilities can be estimated using the auto- and cross-spectrum of the EEG in order to identify brain regions that are deregulated and depart from expected values. Linkage of symptoms and complaints to functional localization in the brain is best achieved by the use of a minimum of 19 channel EEG evaluation so that current source density and LORETA source localization can be computed. Once the linkage is made, then an individualized Z score protocol can be devised. However, in order to make a linkage to symptoms, an accurate statistical inference must be made using the Gaussian distribution. The Gaussian distribution is a fundamental distribution that is used throughout science, e.g., the Schrodinger wave equation in Quantum mechanics uses the Gaussian distribution as basis functions (Robinett, 1997). The application of the EEG to the concept of the Gaussian distribution requires the use of standard mathematical transforms by which all statistical distributions can be transformed to a Gaussian distribution (Box & Cox, 1964). In the case of the EEG, transforms such as the square root, cube root, log₁₀, Box-Cox, and hyperbolic sine are applied to the power spectrum of the digital time series in order to approximate a normal distribution (Duffy, Hughes, Miranda, Bernad, & Cook, 1994; Gasser, Jennen-Steinmetz, Sroka, Verleger, & Mocks, 1988; John, Prichep, & Easton, 1987; John, Prichep, Fridman, & Easton, 1988; Thatcher, North, & Biver, 2005a,b; Thatcher, Walker, Biver, North, & Curtin, 2003). The choice of the exact transform depends on the accuracy of the approximate match to a Gaussian distribution. The fact that accuracies of 95–99% match to a Gaussian are commonly published in the EEG literature encouraged by Thatcher and colleagues to develop and test the Z score biofeedback program.

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Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• List of Contributors
• Chapter 1. History and Technical Foundations of Z Score EEG Biofeedback
• Chapter 2. Network Connectivity and LORETA Z Score Biofeedback
• Chapter 3. Optimal Procedures in Z-Score Neurofeedback: Strategies for Maximizing Learning for Surface and LORETA Neurofeedback
• Chapter 4. Surface and LORETA Neurofeedback in the Treatment of Post-Traumatic Stress Disorder and Mild Traumatic Brain Injury
• Chapter 5. Z-score LORETA Neurofeedback as a Potential Therapy in Depression/Anxiety and Cognitive Dysfunction
• Chapter 6. LORETA Z-Score Neurofeedback in Chronic Pain and Headaches
• Chapter 7. Treating Executive Functioning Disorders Using LORETA Z-Scored EEG Biofeedback
• Chapter 8. Combining LORETA Z-Score Neurofeedback with Heart Rate Variability Training
• Chapter 9. Treating Anxiety Disorders Using Z-Scored EEG Neurofeedback
• Chapter 10. Therapy of Seizures and Epilepsy with Z-Score LORETA Neurofeedback
• Chapter 11. LORETA Neurofeedback in Alcohol Use Disorders: A Case Study
• Chapter 12. LORETA and SPECT Scans: A Correlational Case Series
• Chapter 13. Brainsurfer and Brain Computer Interface Z-Score Biofeedback
• Chapter 14. LORETA Neurofeedback in College Students with ADHD
• Chapter 15. 19-Channel Z-Score Training for Learning Disorders and Executive Functioning
• Index