Post-Traumatic Stress Disorder (PTSD) poses a significant challenge in mental health diagnosis, necessitating innovative and non-invasive approaches. This paper explores the efficacy of emotion recognition through electroencephalography (EEG) as a potential diagnostic tool for PTSD. Leveraging the rich resource of the DEAP EEG database, this study focuses on employing statistical features, namely mean, standard deviation, kurtosis, and Hjorth parameters, to ascertain emotional states associated with PTSD. This work outlines the pressing need for effective and non-invasive PTSD diagnosis methods, emphasizing the potential of emotion recognition as a groundbreaking approach. EEG, with its ability to capture neural activity in real-time, emerges as a promising biomarker for decoding emotional responses associated with PTSD. The paper employs a 1D Convolutional Neural Network (1D CNN) as the classifier algorithm, demonstrating its efficacy in discriminating between valence, arousal, and liking associated with PTSD-related emotional responses. Results indicate a remarkable classification accuracy of 97.18%, highlighting the potential of the proposed approach for PTSD diagnosis. This research contributes a non-invasive diagnostic method, bridging the gap between neuroscience, emotion recognition, and mental health, ultimately paving the way for more effective and accessible PTSD assessment tools.