EEG-Based Multi-Class Emotion Recognition using Hybrid LSTM Approach (International Journal of Innovative Research in Computer Science & Technology, 2023)
motion recognition is a crucial task in  human-computer interaction, psychology, and neuroscience.  Electroencephalogram (EEG)-based multi-class emotion  recognition is a novel approach that aims to identify and  classify human emotions by analysing EEG signals.  Traditional methods of emotion recognition often face  challenges in accurately identifying and classifying human  emotions due to their complexity and subjectivity. EEG based emotion recognition provides a direct and objective  measure of three emotional states (positive, neutral, and  negative), making it a promising tool for emotion  recognition. The proposed hybrid LSTM approach combines  the strengths of different traditional machine learning  algorithms: Gaussian Naive Bayes (GNB), Support Vector  Machine (SVM), Logistic Regression (LR), and Decision  Tree (DT). The approach was tested on the EEG brainwave  dataset, and LSTM achieved an accuracy of 95%, while the  proposed hybrid LSTM-GNB, LSTM-SVM, LSTM-LR, and  LSTM-DT models achieved 65%, 96%, 97%, and 96%  accuracy, respectively. The contribution of this study is the  development of a hybrid LSTM approach that combines the  strengths of two different algorithms, resulting in higher  accuracy for multi-class emotion recognition using EEG  signals. The results demonstrate the potential of the hybrid  LSTM approach for real-world applications such as emotion based human-computer interaction and mental health  diagnosis.
Keywords: EEG Signal; Hybrid LSTM; Emotion Recognition; Brainwave