## 3 Results
### Optimal Imaging Stimulant Results
## 4 Conclusions

This study presents a novel approach to optimizing visual stimuli in EMDR therapy through a data-driven framework utilizing artificial neural networks (ANNs). By integrating key parameters--speed, distance, and size--our ANN model successfully predicted the optimal configurations for maximizing therapeutic effectiveness, as measured by the Predicted Effectiveness Score (PES). Traditional EMDR practices often rely on clinicians' subjective judgment for stimulus design, which can lead to variations in treatment outcomes. In contrast, the ANN-based approach provides an objective, replicable method for stimulus optimization, enhancing the potential for consistent and individualized treatment effectiveness. In this study, we conducted two experiments to empirically examine the feasibility of applying ANN-based techniques to optimize EMDR parameters. We do not propose any new theoretical constructs or introduce additional variables beyond the established speed (\(s_{sp}\)), distance (\(d\)), and size (\(s_{zz}\)) parameters. Instead, our contribution lies in demonstrating how existing methods--ANNs, simulations, and standardized EMDR procedures--can be combined to provide a robust experimental. This approach serves as a stepping stone for future research, rather than a new scientific model. The ANN model's analysis of 2860 parameter combinations identified an optimal configuration (1.8 Hz speed, 1440 px distance, 70 px size), which was subsequently validated through experimental trials. EEG measurements in a sample of participants with depressive symptoms revealed a significant reduction in right frontal beta wave activity when using the optimized stimulus configuration. The PES 1 configuration was 12.31% more effective in reducing beta wave activity compared to PES 50, closely aligning with the model's prediction. This agreement between model predictions and physiological responses underscores the practical utility of ANN-driven stimulus design in EMDR therapy. This research offers a foundational framework for applying machine learning to therapeutic design, moving beyond intuition-based practices. By empirically validating stimulus parameters with real-time physiological data, this study highlights the potential for machine learning tools to refine and enhance EMDR therapy. Furthermore, the

Figure 6: Comparison graph of EO beta wave reduction for PES 50 vs. PES 1.

methodology demonstrated here--optimizing visual stimuli parameters based on ANN predictions--could extend to other therapeutic interventions where neural network models are applicable. This approach not only maximizes the impact of treatment by aligning stimuli with patients' cognitive and physiological responses but also opens new avenues for research and clinical practice. In summary, the findings of this study suggest that ANN-based models offer a valuable and innovative approach to optimizing EMDR stimuli, providing theoretical and practical contributions to the fields of both machine learning and mental health therapy. Future research could explore additional variables and broader patient populations, ultimately supporting the development of highly individualized EMDR protocols that align with each patient's unique therapeutic needs. ## 5 Discussion

This study demonstrates a significant advancement in EMDR therapy by integrating artificial neural networks (ANNs) to optimize the parameters of visual stimuli, specifically speed, distance, and size, which are traditionally determined subjectively by clinicians. The results show that data-driven optimization of these parameters not only enhances the consistency and effectiveness of EMDR therapy but also provides a replicable framework that addresses the variability in treatment outcomes inherent in intuition-based approaches. This discussion examines the broader implications of these findings, the strengths and limitations of this approach, and potential directions for future research and clinical application.