This poster was presented at the International Pharmaco-EEG Group meeting in New York:

Disclaimer: This content 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.

Trait & State-Based Depression Indicators Measured Using Real-Time sLORETA Imaging and Quantification #

Overview #

This study by Bonnstetter, Collura, Bonnstetter, and Hebets (Target Training International, Ltd. & BrainMaster Technologies, Inc.) demonstrates the use of real-time sLORETA imaging to assess trait- and state-based depression indicators. Using BrainAvatar™ software and the VIDE process (Gamma for Ipsative Validation using EEG), researchers quantified emotional asymmetry and neural activation patterns during mood- and emotion-linked responses.

Introduction #

Advancements in real-time sLORETA (standardized Low-Resolution Brain Electromagnetic Tomography) enable dynamic visualization of whole-brain electrical activity associated with emotional and cognitive states.
This study sought to visualize chronic and transient depression patterns through brain activation asymmetry—particularly in prefrontal regions known to mediate emotional tone, avoidance, and approach behaviors.

Methodology #

Participants completed a depression-related online survey, followed by EEG acquisition using BrainAvatar™ and BrainMaster Discovery systems.
Stimuli included emotionally charged prompts (e.g., “sleep,” “smoking,” “favorite foods”). Brain responses were recorded for 1–5 seconds per image, capturing dynamic prefrontal gamma asymmetry linked to emotional processing.

Results #

Researchers identified distinct asymmetry patterns in gamma activity that corresponded with emotionally relevant stimuli.
Key findings include:

• Left prefrontal gamma dominance associated with approach and positive emotional tone.

• Right prefrontal gamma dominance correlated with avoidance or negative mood.

• Each subject displayed a unique “brain activation signature” reflecting personal emotional traits.

The VIDE process validated emotional self-report accuracy by linking objective EEG markers to introspective reports.

Discussion #

Using BrainAvatar™, the process quantifies emotional states by integrating neurophysiological data with subjective self-assessments.
This integration offers a scientific basis for understanding personal decision-making and emotional reactivity, providing a potential clinical framework for:

• Assessing trait vs. state depression,

• Visualizing cognitive-emotional dynamics,

• Enhancing personalized neurofeedback protocols.

Implications for Clinical Practice #

This technology supports quantitative emotional assessment for applications in:

• Clinical evaluation of affective tone,

• Cognitive-behavioral training and self-regulation,

• Neurofeedback personalization via sLORETA-informed Z-score training.

Incorporating BrainAvatar™ and sLORETA imaging can assist clinicians in visualizing and interpreting emotional processing patterns to guide individualized neurotherapy and performance enhancement programs.