Z Score Eeg Biofeedback: Technical Foundations (original) (raw)
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Quantitative EEG and Live Z-Score Neurofeedback- Current Clinical and Scientific Context
?1?2 article discusses the relevance of quantitative EEG (QEEG) and live z-score training (LZT) to the field of mental health in general, and to neurofeedback in particular. We examine what practitioners might learn about clients when QEEG is used for assessment, and the relevance of LZT as a treatment modality. Clinicians can benefit from viewing the brain as a dynamic system, and this point of view can provide a foundation for QEEG and LZT. This approach emphasizes understanding the value of brain activation as a basis for observed symptoms and behaviors. Of paramount importance are localization and frequency information, as well as connectivity metrics. The brain can be viewed as a complex self-controlled system operating with various identifiable networks and frequencies that, when dysregulated, produce what we commonly refer to as ''disorders.''
EEG Biofeedback Case Studies Using Live Z-Score Training and a Normative Database
Journal of Neurotherapy, 2010
This article summarizes clinical results using a neurofeedback approach that has been developed over the last several years and is seeing increasing clinical use. All participants used a form of live Z-score training (LZT) that produces sound and video feedback, based on a computation using a normative database to produce multiple targets. The client receives simple feedback that reflects a
Live ?1 Z-Score training (LZT) neurofeedback is described. Its theoretical underpinnings, technical foundations, and relationship to brain operant learning are described. LZT is an outgrowth of both neurofeedback and quantitative electroencephalography (QEEG) and is consistent with previous QEEG-based neurofeedback methods. In addition, it incorporates elements of self-regulation that allow the client's brain to become part of the decision-making process, providing an important aspect of individualized treatment. LZT encourages flexibility and appropriateness of brain activation and connectivity and is adaptable to a wide range of clients and training needs. Live Z-Score training (LZT) is a form of neurofeedback that incorporates real-time quantitative electroencephalograph (QEEG) information in the form of Z-Scores, as a component in the feedback control mechanism. The Z-Score is a statistic that indicates how many standard deviations the current signal is from the mean on some specific parameter, such as amplitude, power, or coherence. Beyond this one basic principle, there is considerable latitude and variation in precisely how these values are used. It is generally true that the client receives information related to these scores, but further assumptions are not generally valid, without considering the details of a specific method and system. The variations in approaches include whether and how QEEG assessment data are considered, which Z-Scores are selected for training, how Z-Score ranges or windows are defined, what type of algorithm is used to convert Z-Scores into feedback signals, and whether and how outliers are handled. The essential element of LZT is that the EEG is processed to produce estimates of Z-Scores in real time, and these Z-Scores are used to create feedback. The principles were described by Collura and Thatcher (2006). LZT provides a bridge to QEEG and is consistent with other types of QEEG-informed neurofeedback. One key aspect is that deviations from typical EEG activity may be chosen for operant training in the direction of being more typical. For reasons to be explained, we use the concept of typical rather than normal in referring to our reference database population. Unlike traditional neurofeedback in which the QEEG may be used to help to design the training protocol, LZT has the unique advantage of reflecting changes in QEEG information in real time, as training proceeds. It is thus more adaptable and flexible than previous approaches. It is rather like the difference between first reading a map and then undertaking a journey, compared with having a live global positioning system (GPS) system available to monitor and possibly control travel to a designated location. There are many ways to use Z-Scores in neurofeedback, and Z-Scores can be computed for a wide range of EEG parameters. The most common ones include absolute power, relative power, power ratios, asymmetry, coherence, and phase. More recently, with the use of inverse methods to estimate regional brain activity, parameters based on current-source density (CSD) are possible. CSD-based LZT makes possible the ability to train, as well as image, localized brain activity in three dimensions. In addition to metrics related to brain activity (power), it is possible to compute metrics that reflect the connections between specific brain regions, rather than simply between scalp locations. When the connections between two or more regions are measured, we become able to define combinations of regions in the form of networks. Specific networks that serve identifiable tasks are also referred to as hubs, reflecting their functional roles. Because LZT can use many parameters at once, it is intrinsically a multivariate method. Therefore, it can address complex sets of brain activation information in pursuit of directed goals. Rather than simply training ''Theta amplitude down'' or ''to increase Sensorimotor rhythm,'' LZT can address more functionally rich states. When, for example, one trains multiple connectivity metrics at once, in a directed fashion, whole brain function can be managed. To make another analogy, it is like the difference between ''pumping iron'' and ''riding a bicycle.'' Like bike riding, LZT appears to incorporate multiple sets of basic brain skills that can be learned and retained in the interest of overall brain efficiency and flexibility.
Principles and Statistics of Individualized Live and Static Z-Scores
NeuroRegulation, 2020
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.
Introduction to Quantitative EEG and Neurofeedback
American Journal of Psychiatry, 2001
I. INtroduCtIoN this chapter addresses the question of how to classify the neuromodulation effects resulting from widely differing neurofeedback approaches developed over the last four decades. We have seen a proliferation of targets and objectives to which attention is directed in the training. With regard to clinical outcomes, however, one encounters a broad zone of commonality. Why is it that the premises and technological approaches within the neurofeedback network of scholars and clinicians are so disparate, yet they largely achieve common clinical goals? this in-depth analysis may lead us closer to the "essence" of neurofeedback and provide focus for further development efforts. In its most common applications, EEG feedback typically combines two challenges-one directed to the frequency-based organization of brain communication and one that targets inappropriate state transitions. these two challenges lead to very different rules of engagement. As such rules are unearthed, they must be understood in terms of an appropriate model of brain function. At a more philosophical level, an understanding of this whole process also takes us to the very cusp of the mind-body problem, the neural network relations that provide the nexus where our thoughts are encoded and interact directly and inseparably with network representations of psychophysiological states. this chapter will attempt to appraise the "state of the field" at this moment. the objective is to discern the commonalities among the various approaches on the one hand, and among the clinical findings on the other. this will lead to a codification of a "minimal set of claims" that could serve to cover the commonalities among the techniques, and it will lead to a simple classification scheme for the various clinical findings. the evidence in favor of such a minimal set of claims will be adduced largely by reference. Further, the classification of the various
Journal of Neurotherapy, 2013
Advances in neuroscience are applied to the clinical applications of EEG neurofeedback by linking symptoms to functional networks in the brain. This is achieved by reviews of the last 20 years of functional neuroimaging studies of brain networks related to clinical disorders based on positron emission tomography, functional MRI, diffusion tensor imaging, and EEG/MEG inverse solutions. Considerable consistency exists between different imaging modalities because of the property of functional localization and the existence of large clusters of connections in the brain representing network modules and hubs. Reviewed here is new method of EEG neurofeedback called Z-Score Neurofeedback, and it is demonstrated how real-time comparison to an age-matched population of healthy subjects simplifies protocol generation and allows clinicians to target modules and hubs that indicate dysregulation and instability in networks related to symptoms. Z-score neurofeedback, by measuring the distance from the center of the healthy age-matched population, increases specificity in operant conditioning and provides a guide by which extreme Z-score outliers are linked to symptoms and then reinforced toward states of greater homeostasis and stability. The goal is increased efficiency of information processing in brain networks related to the patient's symptoms. The unique advantage of EEG over other neuroimaging methods is high temporal resolution in which the fine temporal details of phase lock and phase shift between large masses of neurons is quantified and can be modified by Z-score neurofeedback to address the patient's symptoms. The latest developments in Z-score neurofeedback are a harbinger of a bright future for clinicians and, most important, patients that suffer from a variety of brain dysfunctions.
Brain Sciences, 2021
Learning disabilities (LDs) have an estimated prevalence between 5% and 9% in the pediatric population and are associated with difficulties in reading, arithmetic, and writing. Previous electroencephalography (EEG) research has reported a lag in alpha-band development in specific LD phenotypes, which seems to offer a possible explanation for differences in EEG maturation. In this study, 40 adolescents aged 10–15 years with LDs underwent 10 sessions of Live Z-Score Training Neurofeedback (LZT-NF) Training to improve their cognition and behavior. Based on the individual alpha peak frequency (i-APF) values from the spectrogram, a group with normal i-APF (ni-APF) and a group with low i-APF (li-APF) were compared in a pre-and-post-LZT-NF intervention. There were no statistical differences in age, gender, or the distribution of LDs between the groups. The li-APF group showed a higher theta absolute power in P4 (p = 0.016) at baseline and higher Hi-Beta absolute power in F3 (p = 0.007) post-treatment compared with the ni-APF group. In both groups, extreme waves (absolute Z-score of ≥1.5) were more likely to move toward the normative values, with better results in the ni-APF group. Conversely, the waves within the normal range at baseline were more likely to move out of the range after treatment in the li-APF group. Our results provide evidence of a viable biomarker for identifying optimal responders for the LZT-NF technique based on the i-APF metric reflecting the patient’s neurophysiological individuality