Recognizing emotional states from electroencephalogram, or Electroencephalogram (EEG), signal data is challenging due to its large dimension and intricate spatial dependencies. Our project illustrates a novel approach to Electroencephalogram (EEG) data analysis in emotion recognition tasks that employ Dynamic Graph Convolutional Neural Networks (DGCNN). Our novel architecture takes advantage of the inherent graph structure of Electroencephalogram (EEG) electrodes to effectively capture spatial relationships and dependencies. Our approach used a refined DGCNN model to process and classify the data into four primary emotional states-Happy, Sad, Fear, and Neutral, we configured the DGCNN with 20 input features per electrode, optimized across 62 electrodes, and utilized multi-layered graph convolutions. The model achieved an overall classification accuracy of 97%, with similarly high macro and weighted average scores for precision, recall, and F1-score, demonstrating its resilience and accuracy.