EEG-Based Emotion Classification and Personal Response Optimization Using SVM

Humans experience a range of emotions during connection and communication, and these feelings differ in complexity, meaning, and intensity. Although behavioral emotions can also be expressed through body language, tone of voice, facial expressions, and other means, it can be challenging to identify the emotions in people who are unable to do so. In these cases, brain signals can be used to identify the emotions. In this research, we used a signal that the human brain creates based on an emotion's state to classify the emotions. To identify emotions, we employed EEG Eight frequency band signals. Different emotions, including joyful, sad, angry, fear, surprise, neutral, and disgust, were explained and recognized through expressions. by playing five minutes of these seven Indian films that focus on emotions, measuring the intensity of each emotion using brain signals, and comparing the results to the subject's remark. For this, we selected 35 Indian movie clips based on seven emotions and 50 individuals between the ages of 18 and 50. In order to improve accuracy, we used two techniques for emotion detection: one for answering the questions and another for classifying brain signals using support vector machines.