EEG-Based Emotion Detection Using Prefrontal Cortex Activity: A Hybrid CNN–LSTM Model

Abstract

This paper presents an innovative method for recognizing emotion from EEG data from the left hemisphere of the prefrontal cortex. A hybrid deep learning model using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) is proposed. The DEAP dataset and custom dataset (MindData) used in this paper contains EEG data of emotional stimuli. The relevant features are extracted using a 1D convolutional layer to capture local temporal changes and then maxPooling1D to reduce dimension. The sequential dependencies are captured by using an LSTM layer. The final emotion is classified using a softmax layer. The feature extraction has been performed using the time domain, frequency domain, time-frequency domain, and Multi-electrode features. A hybrid approach that combines decision trees and Nearest-Neighbor classification using Random Forest (RF) and K-Nearest Neighbor (KNN) improves accuracy. Trained the model using the Adam optimizer and sparse categorical cross-entropy loss over 100 epochs. The results showed that the combined CNN-LSTM model improves the accuracy of emotion detection from EEG signals. The proposed hybrid model increases the accuracy of the classifications as compared to the traditional CNN and Recurrent Neural Network (RNN) models. This paper highlights the effectiveness of combining CNN and LSTM methods for emotion recognition and has potential applications in neurofeedback, affective computing, and real-time emotion recognition.