Enhanced Cross-Dataset Electroencephalogram-Based Emotion Recognition Using Unsupervised Domain Adaptation

January 1, 2025·
Md Niaz Imtiaz
Naimul Khan
Naimul Khan
· 0 min read
Abstract
We propose an enhanced unsupervised domain adaptation framework for EEG-based emotion recognition that achieves robust cross-dataset generalization without requiring target-domain labels. The method addresses the significant distribution shift between EEG recordings across subjects and datasets.
Type
Publication
Computers in Biology and Medicine, 184, 109394
publications
EEG Emotion Recognition Domain Adaptation Affective Computing
Authors
Md Niaz Imtiaz
Post-Doctoral Fellow
Recently completed his PhD with Dr. Khan. Expert in physiological signal analysis and unsupervised domain adaptation, with a focus on cross-dataset generalization for ECG and EEG-based applications.
Naimul Khan
Authors
Naimul Khan
Associate Professor & Lab Director
Associate Professor and Director of the Multimedia Research Laboratory at Toronto Metropolitan University. Research spans multimedia signal processing, machine learning, and AR/VR with applications in healthcare and mental health.