Enhanced EEG-based Emotion Classification using Convolutional Neural Networks with Novel Feature Integration

Abstract

The rapidly developing endeavor of EEG-based emotion classification within the domain of affective computing is one that has potential application in mental health diagnostics, human-computer interaction, and adaptive systems. Nevertheless, as emotional states are complex and non-linear concepts, identifying specific emotions obviously remains a big problem that needs complicated methods to provide the required features of EEG patterns. Though Convolutional Neural Networks (CNNs) have been shown to be an effective method for EEG based classification, their success is heavily dependent on the feature quality and diversity. In this thesis, we propose a new multi-feature CNN model which consists of differential entropy (DE) and power spectral density (PSD), as well as Mel frequency cepstral coefficients (MFCCs) that is an audio signal feature, conventionally unused for EEG classification. With the fusion of these feature sets, it leverages the recognition of emotion using EEG signals The model was tested on both SEED-IV and the SEED datasets, outperforming baselines by a wide margin for the first time. It attained an accuracy of 77.51% on the SEED-IV dataset and has outperformed the CGCNN model by a margin having the accuracy of 75.48% using DE alone for emotion classification, therefore setting new state of the art in emotion classification. In addition, it showed even better validation results on the SEED dataset which is a 3-class less-complex dataset getting the accuracy of 93.87% against CGCNN 93.36% proved the robustness of the model. The incorporation of MFCCs with DE and PSD greatly improves the performance and stability of CNN-based emotion recognition, making this work a complete solution to common real time adaptive emotional classification systems.