Have you ever wondered what goes on inside our brains when we experience different emotions? Well, it turns out that our thoughts, feelings, and physiological changes are all connected. In a recent study, researchers used electroencephalography (EEG) signals, which capture the electrical activity of the brain, to analyze emotions. They were particularly interested in how these signals relate to Post-Traumatic Stress Disorder (PTSD). This disorder can cause long-term suffering and impairments in emotional well-being, and it affects the brain’s response to traumatic memories and emotions. By examining EEG signals, researchers can gain insights into the electrical patterns associated with different emotions. However, previous studies have faced challenges in accurately analyzing emotions due to reliability issues and masking of true emotional behavior. To address these gaps, the researchers developed a new approach using a hybrid deep learning algorithm called CNN-LSTM with ResNet-152. This innovative technique achieved an impressive accuracy level of 98%, outperforming existing methods. The findings of this study not only enhance our understanding of emotions but also hold potential for improving the diagnosis and treatment of PTSD. If you’re curious to learn more about this exciting research, be sure to check out the full article!

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.

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