This paper will provide an update on the state of emotion recognition using EEG signals from 2016 to 2019, since the last thorough review was carried out between 2009 and 2016. This cutting-edge review concentrates on the components of study size, EEG hardware, machine learning classifiers, presentation strategy, and emotion stimuli type. Based on this state-of-the-art review, we propose various avenues for future research, such as taking a different tack when presenting the stimuli using virtual reality (VR). Considering this, a new section that reviews only VR studies in this field of study is provided, serving as inspiration for the suggested method of presenting stimuli through the use of virtual reality. Our proposed methodologies also highlight the integration of advanced neural network architectures, such as Gated Recurrent Unit network (GRU) that is a type of recurrent neural network (RNN), which has shown promising results in emotion recognition tasks. These approaches offer improved accuracy and robustness, setting the stage for more nuanced and reliable emotion recognition systems. We have gathered EEG dataset for emotion recognition from Kaggle.