Foundations of Neuronal Dynamics and Z Scores.pdf

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.

Overview #

This presentation, given by Thomas F. Collura, PhD (BrainMaster Technologies, Inc.), provides a foundational explanation of EEG generation, quantitative EEG (qEEG) concepts, and the statistical framework for z-score analysis. It outlines how neuronal activity produces measurable signals, how qEEG metrics are derived, and how z-scores enable standardized interpretation and training.

1. Fundamentals of EEG Generation #

1.1 Neuronal and Population Dynamics #

• EEG signals originate from neuronal dipole activity.

• Synchrony among neuron populations amplifies detectable signals.

• Brain anatomy, physiology, and conductive tissues influence recorded potentials.

1.2 EEG Rhythms and Inhibition #

• Cortical rhythms arise largely through inhibitory relaxation and thalamo-cortical reverberation.

• Populations oscillate together during relaxed or synchronized states.

2. EEG Measurement Framework #

2.1 Sensor Placement & Montages #

• Uses the international 10–20 system with referential (e.g., linked ears) or bipolar montages.

2.2 EEG Component Bands #

Typical frequency ranges include delta (1–4 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta sub-bands, and gamma (>40 Hz).

2.3 Core EEG Metrics #

Amplitude, frequency, percent energy, variability, coherence, phase, and asymmetry are primary qEEG measures.

3. qEEG Analysis Methods #

3.1 Digital Filtering #

Fast, predefined bands; ideal for live training.

3.2 Fast Fourier Transform (FFT) #

Slower but comprehensive frequency analysis; suitable for assessments.

4. MINI-Q Overview #

A 2-channel head-scan method covering 12 sites for streamlined assessment. Integrated with tools such as DCN128 and NeuroGuide for normative and z-score analysis.

5. Z-Score Foundations #

5.1 Purpose of Z-Scores #

• Enables comparison of an individual’s EEG features to a normative population.

• Quantifies deviation from mean using standardized statistics (mean, SD).

• Applied to power metrics, asymmetry, coherence, and phase.

5.2 Z-Score Interpretation #

• ±1 SD covers ~68% of the population

• ±2 SD covers ~95%

• ±3 SD covers ~99.8%
  Extremes reflect greater deviation from normative expectations.

6. Real-Time Z-Score Training #

6.1 Multi-Channel Targeting #

• 2-channel: 76 possible z-score targets

• 4-channel: 248 possible targets
  Includes absolute/relative power, ratios, asymmetry, coherence, and phase.

6.2 Training Approaches #

• Directional training (up/down)

• Rng() functions for range-based targeting

• PercentZOK() to calculate the percentage of z-scores within desired limits

• Adaptive protocols blending amplitude and connectivity metrics automatically.

6.3 Z-Score Protocol Characteristics #

• Eliminates manual thresholding

• Dynamically adjusts based on most deviant features

• Integrates QEEG analysis with training in real time, consistent with established QEEG-guided practices.