Quantum Stress Monitoring: Wearable In-Ear EEG Powered by Hybrid Quantum-Classical Convolutional Neural Networks

This study presents an innovative framework for remote stress monitoring that integrates in-ear electroencephalography (EEG), hybrid quantum-classical convolutional neural networks (QC-CNNs), and Internet-of-Medical-Things (IoMT) technologies. Leveraging in-ear EEG signals, the proposed system addresses the limitations of conventional scalp EEG systems, such as bulkiness and user discomfort, while enabling continuous, unobtrusive monitoring. The QC-CNN model synergistically combines quantum computing (QC) and deep learning to enhance stress classification performance. Key contributions of this work include: 1) the development of a comprehensive in-ear EEG system comprising both hardware and software components; 2) the introduction of a novel time–frequency signal-to-scalogram conversion technique based on the fractional adaptive superlet transform (FASLT); 3) a comparative evaluation of the QC-CNN model against traditional neural networks; and 4) the implementation of an IoMT-enabled platform for real-time remote stress monitoring and management. The system was validated through experiments involving stress-inducing tasks such as the Stroop color-word test, mental arithmetic, and memory recall. Results demonstrated strong classification performance, with 0.906 accuracy using leave-one-out cross validation (LOOCV) and 0.942 using tenfold cross validation (CV), alongside high sensitivity, specificity, precision, and $F1$ -scores. The beta frequency band emerged as the most informative for stress detection, and right-ear EEG signals yielded superior accuracy compared with left-ear recordings. This study underscores the potential of integrating in-ear EEG, QC-CNN models, and IoMT frameworks for effective real-time stress monitoring, offering a promising avenue for personalized healthcare and stress management.