Quantifying human emotions presents a significant challenge due to their complex, subjective, and dynamic nature. However, successfully measuring and interpreting these emotions can yield substantial benefits. Emotion quantification has a wide range of impactful applications, spanning from clinical settings—such as mental health monitoring and therapy personalization—to engineering domains like human-computer interaction and affective computing, as well as in entertainment, where it can enhance user experience through emotionally responsive content. Emotion quantification has been explored through various modalities; however, electroencephalography (EEG) emerges as the most accurate and scientifically robust noninvasive technique, owing to its high temporal resolution. With the implementation of appropriate emotion recognition algorithms, EEG data can be effectively utilized to develop a portable and versatile emotion quantification system. This research focuses on a comprehensive literature survey aimed at identifying and synthesizing key methodologies to support the design of such a portable system for diverse real-world applications.