International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 | Dec 2025
p-ISSN: 2395-0072
www.irjet.net
REAL-TIME EEG-BASED EMOTION RECOGNITION USING DEEP LEARNING AND STACKED META-LEARNING Mayur G N1, Kumar Swamy S2 1 Student , M.Tech IT, Dept. of Computer Science, University of Visvesvaraya College of Engineering, Bengaluru,
India
2 Dr. Kumar Swamy S,Associate Professor, Dept. of Computer Science, University of Visvesvaraya College of
Engineering, Bengaluru, India ---------------------------------------------------------------------***------------------------------------------------------------------
Abstract - This work presents a machine learning–based
often relies on manual inspection and handcrafted analysis, which is time-consuming and demands significant domain expertise.
framework for EEG-based emotion recognition using a structured pipeline of preprocessing, feature extraction, and classification. EEG signals are processed through band-pass filtering and artifact reduction, followed by the extraction of statistical features (mean, variance, skewness, kurtosis), temporal features (Hjorth activity, mobility, complexity), and frequency-domain measures (delta, theta, alpha, beta, and gamma band power). These features are utilized by classical classifiers such as Support Vector Machines (SVM) and Random Forest (RF), along with deep learning models based on Convolutional Neural Networks (CNNs). To enhance classification performance, the framework incorporates transfer learning using pretrained CNN architectures such as VGG and Res Net, along with a stacked meta-learning approach combining Logistic Regression and XG Boost for effective fusion of multiple model outputs. Class imbalance is addressed using SMOTE and class-weighted learning to ensure fair emotion classification. A SHAP-based explainability module is integrated to provide transparent interpretation of model predictions. The system is deployed through a Flask-based web interface supporting EEG uploads, real-time inference, visualization, and historical emotional analysis. The proposed architecture demonstrates accurate, robust, and interpretable emotion recognition suitable for mental health and cognitive applications.
Fig-1: EEG Based Emotion Recognition System To overcome these limitations, this work presents an intelligent machine learning–driven framework for automated and real-time EEG-based emotion recognition. The proposed system integrates signal preprocessing, feature extraction, multi-model classification, and visualization within a unified and user-friendly web-based platform. Advanced learning strategies, including deep learning and model fusion, are employed to improve classification robustness and generalization across emotional states. In addition, the framework emphasizes interpretability and real-time usability, enabling transparent emotional assessment suitable for practical applications in mental health monitoring and affective computing.
Key Words: EEG Signals, Emotion Recognition, Deep
Learning, Stacked Meta-Learning, Transfer Learning, Feature Extraction, SHAP Explainability, Real-Time EEG Analysis, Affective Computing
1. INTRODUCTION Emotion recognition using physiological signals has attracted considerable research interest due to its importance in mental health monitoring, cognitive analysis, and human–computer interaction. Among various bio signals, electroencephalogram (EEG) signals offer a non-invasive and reliable means of capturing brain activity, enabling the identification of emotional states through underlying neural patterns. Unlike external behavioral cues, EEG reflects unconscious brain responses, making it particularly suitable for accurate and objective emotion analysis. However, conventional EEG evaluation
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1.1 Motivation Early detection of emotional patterns is essential for understanding mental well-being and preventing the escalation of stress-related conditions. Traditional evaluation methods rely heavily on subjective feedback or clinical observation, which can result in inconsistency and limited insight into rapid or subtle emotional changes. With recent advancements in machine learning and deep learning, there is a strong opportunity to improve
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