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Emotion is a complicated state that influences one's thoughts and behaviour. Recognizing the emotions of a human being is a major research interest in the affective computing after this pandemic situation and which can be applied in medical related fields to cure physical and mental illness. Early detection of stress helps humans to avoid or prevent many diseases related to it. The development of an emotion recognition system using machine learning algorithms has taken a lot of time and effort for researchers and is less focused with Electroencephalography (EEG) signals because EEG signals are noisy, non-linear, and non-stationary. Deep learning algorithms are the most popular solution due to its ability to see images as data. In this paper, we propose a deep learning framework, Gated Recurrent Unit Emotion Recognizer (GRUER) that can detect human emotion with help of EEG signals. This is achieved by …