  Emotion has a significant influence on how one thinks and interacts with
others. It serves as a link between how a person feels and the actions one
takes, or it could be said that it influences one's life decisions on occasion.
Since the patterns of emotions and their reflections vary from person to
person, their inquiry must be based on approaches that are effective over a
wide range of population regions. To extract features and enhance accuracy,
emotion recognition using brain waves or EEG signals requires the
implementation of efficient signal processing techniques. Various approaches to
human-machine interaction technologies have been ongoing for a long time, and
in recent years, researchers have had great success in automatically
understanding emotion using brain signals. In our research, several emotional
states were classified and tested on EEG signals collected from a well-known
publicly available dataset, the DEAP Dataset, using SVM (Support Vector
Machine), KNN (K-Nearest Neighbor), and an advanced neural network model, RNN
(Recurrent Neural Network), trained with LSTM (Long Short Term Memory). The
main purpose of this study is to improve ways to improve emotion recognition
performance using brain signals. Emotions, on the other hand, can change with
time. As a result, the changes in emotion over time are also examined in our
research.
