Emotion identification and categorization have been emerging in the Brain Machine Interface in current era. Audio, visual, and electroencephalography (EEG) data have all been shown to be useful for automated emotion identification in a number of studies. EEG-based emotion detection is a critical component of psychiatric health assessment for individuals. If EEG sensor data are collected from multiple experimental sessions or participants, the underlying signals are invariably non-stationary. Because EEG signals are noisy, non-linear, and non-stationary, developing an intelligent framework that can give high accuracy for emotion identification is a difficult challenge. Many research has shown evidence that EEG brain waves may be used to determine feelings. This study introduces a novel automated emotion identification system that employs deep learning principles to recognize emotions through EEG signals from computer games. EEG data was obtained from 28 distinct participants using a 14-channel Emotive Epoc+ portable and wearable EEG equipment. Participants played four distinct emotional computer games for five minutes each, with a total of 20 minutes of EEG data available for each participant. The suggested framework is simple enough to categorize four classes of emotions during game play. The results demonstrate that the suggested model-based emotion detection framework is a viable method for recognizing emotions from EEG data. The network achieves 99.99 along with less computational time.