Emotion Detection today is one of the most important directions for research papers in any specific field. It performs a very important role in organizing human-computer interaction. We as humans are able to understand the emotions of other person but it is impossible for the computer to do so. The current work is to meet the same as accurately as possible. Emotion detection can do either through textual content, speech, facial expression or features [1]. EEG is a test that measures and records the electrical activity of our brain using special sensors [2]. The computer records the brain electrical activity as wavy lines. Positive situations, such as seizures, possible to seen by the changes in the everyday pattern of the brain’s electrical activity. EEG helmet uses a set of sensors to measure the signal changes generated by the brain to discover people’s thoughts, feelings and expressions and connects wirelessly to the personal computer, It has 14 sensors which a put on particular places on the scalp to give acceptable detection accuracy [3]. Human emotion is very hard to find simply through looking on the face and additionally the conduct of someone [4]. This research paper conducted to discover or perceive human emotion through the take a look at the of brain waves. Additionally, the research paper aims to improve software program that can detect human emotions fast. This study aims at EEG signals of relationship and human feelings. Brain waves produced by a human to change from one person to another depending on their current activities. For instance, the brain waves of the relaxed person are different from the waves of the person doing the hard work and bad mood. These brain waves classified into five classification indicating various conditions which Delta, theta, alpha, beta, and gamma [1]. In our research paper, we mainly try to discover the relationship between EEG and human emotions. Instead of using the face directly, we apply a dynamic programming to extract the maximum number of quality service that provides to the user when the device captures specific signals for each emotion. The aim of this operation is to decrease the impact of factors irrelevant to emotion. In order to discover the features which are most related to emotion, we reduce feature measure through correlation degree whilst keeping a stable classification execution, and figure out the brain areas and frequency constraint according to with the positions to which they exposed. In addition to, we try to find the track of emotion changes with multiple models. The structure of this paper as follows. In Section II we present some related works. In Section III show the methodology and experimental design. In Section IV illustrates the overview of the results and discussion of this present work. Finally, Section V summarizes the conclusions and briefly highlights future work.