Advanced EEG emotion recognition framework integrating fractal dimensions, connectivity metrics, and domain adaptive deep learning

Faris Abuhashish (), Riyad Alrousan (), Hamzah A. Alkhazaleh (), Anas W. Arram () and Ismahafezi Ismail ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 6, 1247-1265

Abstract: This study proposes an advanced framework for EEG-based emotion recognition to address challenges posed by subject variations and signal complexity, aiming to enhance mental health monitoring and human-computer interfaces. A comprehensive feature set was developed, integrating Fractal Dimensions (FD), Phase Locking Value (PLV), Pearson Correlation Coefficient (PCC), and Short-Time Fourier Transform (STFT). The framework employs both conventional classifiers (SVM, Linear Regression) and deep learning models (CNN, DA-RCNN), with a particular emphasis on domain adaptation within DA-RCNNs to mitigate inter-subject variability. Evaluation involved 10-fold cross-validation and rigorous statistical tests. The DA-RCNN model achieved a balanced accuracy of 94.5%, demonstrating competitive or superior performance compared to existing methods. Feature integration significantly improved classification, with FD features boosting accuracy to 94.5% and connectivity measures contributing an additional 7.2%. The approach exhibited computational efficiency and reduced reliance on extensive data augmentation. The proposed framework successfully integrates diverse features and domain adaptation techniques for robust EEG-based emotion recognition, marking a significant advancement in affective computing and neuroscience. The framework's computational efficiency and real-time applicability offer substantial utility for mental health monitoring, adaptive interfaces, and human-computer interaction across diverse populations and operational scenarios.

Keywords: EEG; Emotion recognition; Feature extraction; Deep learning; Domain adaptation. (search for similar items in EconPapers)
Date: 2025
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