## 1 Introduction  
In 2013, the World Health Organization identified Eye Movement Desensitization and Reprocessing (EMDR) as an evidence-based method for treating post-traumatic stress disorder (PTSD) [1]. EMDR, a non-pharmacological intervention, has been shown to facilitate faster psychological relief compared to traditional therapies. Initially developed by [PERSON] for PTSD, EMDR has since been expanded to other areas of psychotherapy, including stress reduction, panic disorders, anxiety, and anger management [2, 3, 4, 5]. [PERSON] emphasized EMDR's effectiveness in addressing negative life experiences and alleviating depressive symptoms, with 24 randomized controlled trials supporting its efficacy in treating emotional trauma and various psychological disorders. These findings indicate that EMDR therapy has applications beyond trauma treatment, extending to a broaderrange of physical and emotional stressors, which supports its adoption in psychological and medical settings [6]. A recent meta-analysis further demonstrated that EMDR is comparable to cognitive behavioral therapy (CBT) in terms of quality-of-life (QoL) improvement, particularly in managing psychological stress. It was also found to significantly reduce depressive symptoms among individuals with major depressive disorder (MDD) [7]. Despite the strong evidence supporting EMDR's effectiveness, there is limited guidance on the design of displays and stimuli used in EMDR sessions. To enhance EMDR therapy's efficacy, immersive display technologies like head-mounted displays (HMDs) could be explored over traditional monitor-based displays. Preliminary studies on virtual reality exposure therapy (VRET) using HMDs suggest its potential in improving PTSD outcomes, though that research focuses on VRET rather than EMDR. Furthermore, that research utilized VR technology to present PTSD stimuli, thereby creating an immersive environment for participants [8]. In EMDR, visual stimuli parameters such as speed, distance, and size are thought to influence treatment effectiveness by modulating eye movement frequency, but specific guidelines on these parameters remain subjective. For instance, prior studies examined the effects of slower (0.8 Hz) and faster (1.2 Hz) eye movements on working memory but did not target individuals with psychological conditions, nor did they directly address EMDR [9; 10]. As EMDR aims to desensitize negative memories and emotions by overloading working memory, insights from working memory load studies (e.g., [PERSON] and Hitch) and attentional span research (e.g., Yerkes-Dodson law) have been integrated into EMDR, though no studies have systematically optimized video stimulus parameters [11; 12; 13]. [PERSON] provided preliminary guidelines for stimulus size and distance, but these ranges are broad and lack empirical specificity [2]. To bridge this gap, this study conducts two experiments to refine EMDR therapy parameters:  
1. EMDR Video Stimulus Optimization through Neural Network Regression Modeling. 2. Verification of Experiment 1 with electroencephalography (EEG) measurements on participants with confirmed depressive symptoms. In the first experiment, an artificial neural network (ANN) based on the Adaptive Information Processing (AIP) model was developed. We adopt an artificial neural network (ANN) methodology within the Adaptive Information Processing (AIP) framework to experimentally investigate EMDR parameters [14; 15]. Although we do not introduce a new theory, we emphasize that a thorough grasp of ANN fundamentals--such as learning rates, activation functions, and model architecture--is essential before clinical implementation. Here, the size, speed, and distance of EMDR video stimuli were defined as independent variables, while the Predicted Effectiveness Score (PES) served as the dependent variable. In this study, we define three key parameters for EMDR-based interventions: speed (s\({}_{\text{sp}}\)), distance (d), and size (s\({}_{\text{sz}}\)).