  One of the most important study areas in affective computing is emotion
identification using EEG data. In this study, the Gated Recurrent Unit (GRU)
algorithm, which is a type of Recurrent Neural Networks (RNNs), is tested to
see if it can use EEG signals to predict emotional states. Our publicly
accessible dataset consists of resting neutral data as well as EEG recordings
from people who were exposed to stimuli evoking happy, neutral, and negative
emotions. For the best feature extraction, we pre-process the EEG data using
artifact removal, bandpass filters, and normalization methods. With 100%
accuracy on the validation set, our model produced outstanding results by
utilizing the GRU's capacity to capture temporal dependencies. When compared to
other machine learning techniques, our GRU model's Extreme Gradient Boosting
Classifier had the highest accuracy. Our investigation of the confusion matrix
revealed insightful information about the performance of the model, enabling
precise emotion classification. This study emphasizes the potential of deep
learning models like GRUs for emotion recognition and advances in affective
computing. Our findings open up new possibilities for interacting with
computers and comprehending how emotions are expressed through brainwave
activity.
