Emotions play an important role in the health and well-being of humans. It is associated with feedback on human interaction with the surrounding environment, decision-making, and intelligence. Electroencephalography (EEG)-based brain-computer interfaces (BCI) technology can be used to sense the emotional state of humans. Therefore, this research introduces a non-invasive BCI system that provides solutions for psychiatrists to treat patients suffering from chronic sadness, depression, and anxiety without medications. Here, we propose an EEG-based neurofeedback system for decoding and modulating human emotions. This system decodes three emotions: happiness, sadness, and neutral emotions. From the decoded emotion, the system generates visual and auditory feedback to train the patient to regulate his/her brain activity to improve his/her mental health. We collected EEG data corresponding to each emotion from twelve female participants while watching multiple stimuli to develop a support vector machine (SVM) model with a radial basis function (RBF) kernel. The SVM model decoded the desired emotions with 92.3% accuracy. Then, EEG- Neurofeedback sessions decode the patient's emotions in real-time and generate visual and auditory feedback using the decoded emotions.