During engagement or communication, the human body produces a range of emotions that differ in complexity, intensity, and meaning. It may be simple to interpret behavioral emotions communicated through body language, voice tonality, and facial expressions, but when it comes to those who are unable to express their feelings through behavior, emotions can still be detected by a person's brain signal. In this study, we used a signal that the human brain generates based on emotional state to categorize emotions. For emotion detection, we employed EEG signals from the eight frequency bands. Different emotions, including joyful, sad, angry, fearful, astonished, and neutral expressions, were recognized. by displaying various emotional video clips, measuring the intensity of the emotion using brain signals, and contrasting it with the subject's own words. We have prepared a dataset of 100 people's brain signals as well as 40 different emotion-based movie clippings. Apply For the purpose of calculating and comparing results, support vector machines and deep learning networking were used.