Janki Broker
Electroencephalogram (EEG) signals for emotion classification have gained significant attention in recent times due to its implicit applications in varied fields, including human- computer interaction, affective computing, and substantiated counseling sessions. This study set out to explore the effectiveness of convolutional neural networks (CNNs) in classifying emotions using the DEAP dataset, a publicly available repository of EEG recordings and corresponding emotional annotations by experimenting with different optimizers and loss functions, like "Adamax" and "binary_crossentropy,", etc. By carefully manipulating these optimizers, the study aims to examine and determine the optimal combination with the highest classification accuracy. The findings contribute valuable insights into fine- tuning CNN models for emotion classification based on hyperparameters, paving the way for more robust and effective applications such as personalized diagnosis for counseling, stress- busting techniques and so on. The promising result of this research shows that the “Adamax” optimizer outperforms other optimizers by 82.98 % of the accuracy of classification.