Disclaimer: The content below was generated with the assistance of AI and then reviewed and edited by BrainMaster Technologies, Inc. It is provided for educational and informational purposes only and does not constitute medical advice.
1. Introduction #
This document provides a comprehensive technical overview of how neuronal activity generates measurable EEG signals and how statistical normalization—specifically z-scores—supports standardized interpretation in quantitative EEG (QEEG) and neurofeedback. It explains the biophysics of EEG generation, EEG analysis methods, normative databases, and the principles of real-time z-score–based training.
2. Electrophysiology and EEG Signal Generation #
2.1 Neuronal Potentials and Population Dynamics #
The foundation of EEG arises from postsynaptic potentials in cortical pyramidal neurons. Population synchrony enhances signal strength, making large-scale neuronal oscillations detectable at the scalp.
2.2 Propagation to Scalp Electrodes #
Signals travel through brain tissue, cerebrospinal fluid, skull, and scalp, undergoing amplitude reduction and spatial “blurring,” as illustrated by diagrams of current flow and field spread (pages 6–7).
3. EEG Architecture and Measurement #
3.1 Cortical Layers & Source Orientation #
Images on pages 4–5 show cortical laminar structures and pyramidal cell dipoles that underpin surface EEG detection.
3.2 Electrode Placement & Montages #
The document reviews the 10–20 system (p.10) and contrasts referential vs. bipolar montages, including common reference selections.
3.3 EEG Rhythms & Metrics #
Typical frequency bands include delta, theta, alpha, beta (sub-bands), and gamma (p.16). Key quantitative metrics include amplitude, frequency, percent energy, coherence, phase, and asymmetry (p.17).
4. Quantitative EEG (QEEG) and MINI-Q Assessment #
The MINI-Q system scans 12 sites using a 2-channel acquisition, enabling preliminary assessment and limited z-score analysis (pages 18–20). It supports manual inspection, DCN128 tools, NeuroGuide norms, and real-time z-score visualization.
5. Foundations of Z Scores in QEEG #
5.1 Concept of Normative Statistics #
Z-scores quantify how far a measurement deviates from a population mean using standard deviation units. The document includes equations (p.28), explanations (p.29), and visualizations of normal distributions (pages 24–27).
5.2 Application in EEG #
Z-scores are calculated for:
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Absolute & relative power
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Power ratios
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Asymmetry
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Coherence
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Phase
Different frequency bands are defined specifically for the z-score DLL (p.34). Multi-channel setups can produce between 76 and 248 real-time z-score targets (pages 36–38).
6. Z-Score Neurofeedback Training #
6.1 Targeting Approaches #
Training can involve directional z-score shaping, range-based targeting, PercentZOK functions, and combined amplitude/coherence contingencies (pages 39–45).
6.2 Percentage-Based Adaptive Training #
Percent-within-range strategies adjust feedback based on:
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Z-score window size
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Required hit percentage
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Time-in-criterion (p.47)
Changes to thresholds and window size demonstrate predictable effects on training difficulty (pages 48–49).
6.3 Integration With Real-Time QEEG #
Z-score methods replace traditional thresholding, integrating assessment and training. Protocols automatically adapt to the most deviant metrics (p.50).
7. Supporting Literature #
The reference list (p.51) cites foundational QEEG normative database work by Thatcher et al., as well as ISNR position papers establishing standards for QEEG and neurofeedback.
8. Conclusion #
This document establishes the scientific and statistical foundations for modern QEEG-guided neurofeedback. It explains how neuronal generators create EEG rhythms, how quantitative metrics are derived, and how z-score normalization enables standardized assessment and dynamic, real-time neurofeedback targeting.
