Unveiling Emotions from EEG: A GRU-Based Approach

TL;DR

This paper demonstrates that a GRU-based deep learning model can accurately classify emotional states from EEG data, achieving 100% validation accuracy and outperforming other machine learning methods.

Contribution

It introduces a novel application of GRU neural networks for emotion recognition from EEG signals, with comprehensive preprocessing and high classification accuracy.

Findings

Achieved 100% validation accuracy with the GRU model

Outperformed other machine learning techniques in emotion classification

Provided detailed analysis via confusion matrix

Abstract

One of the most important study areas in affective computing is emotion identification using EEG data. In this study, the Gated Recurrent Unit (GRU) algorithm, which is a type of Recurrent Neural Networks (RNNs), is tested to see if it can use EEG signals to predict emotional states. Our publicly accessible dataset consists of resting neutral data as well as EEG recordings from people who were exposed to stimuli evoking happy, neutral, and negative emotions. For the best feature extraction, we pre-process the EEG data using artifact removal, bandpass filters, and normalization methods. With 100% accuracy on the validation set, our model produced outstanding results by utilizing the GRU's capacity to capture temporal dependencies. When compared to other machine learning techniques, our GRU model's Extreme Gradient Boosting Classifier had the highest accuracy. Our investigation of the confusion matrix revealed insightful information about the performance of the model, enabling precise emotion classification. This study emphasizes the potential of deep learning models like GRUs for emotion recognition and advances in affective computing. Our findings open up new possibilities for interacting with computers and comprehending how emotions are expressed through brainwave activity.