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Emotion has a significant influence on how one thinks and interacts withothers. It serves as a link between how a person feels and the actions onetakes, 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 toperson, their inquiry must be based on approaches that are effective over awide range of population regions. To extract features and enhance accuracy,emotion recognition using brain waves or EEG signals requires theimplementation of efficient signal processing techniques. Various approaches tohuman-machine interaction technologies have been ongoing for a long time, andin recent years, researchers have had great success in automaticallyunderstanding emotion using brain signals. In our research, several emotionalstates were classified and tested on EEG signals collected from a well-knownpublicly available dataset, the DEAP Dataset, using SVM (Support VectorMachine), KNN (K-Nearest Neighbor), and an advanced neural network model, RNN(Recurrent Neural Network), trained with LSTM (Long Short Term Memory). Themain purpose of this study is to improve ways to improve emotion recognitionperformance using brain signals. Emotions, on the other hand, can change withtime. As a result, the changes in emotion over time are also examined in ourresearch.

Emotion Analysis on EEG Signal Using Machine Learning and Neural Network