Emotion analysis using machine learning model and deep learning model on DEAP dataset

Abstract

Emotion has a signi cant in uence on how you think and interact with others. It serves as a link between how you feel and the actions you take, or you could say it in uences your life decisions on occasion. Since the patterns of emotions and their re ections vary from person to person, their inquiry must be based on approaches that are e ective 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 e cient 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 classi ed 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 use improved 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 through time are also examined in our research.