A Wearable BTE-EEG Embedded Device for Emotion Monitoring with Quantum Machine Learning

Emotional disorders present significant challenges in both mental health research and clinical practice, underscoring the critical need for effective recognition and intervention strategies. This study introduces a groundbreaking approach that employs a behind-the-ear (BTE) EEG system for emotion recognition, utilizing quantum machine learning techniques. The research encompasses a comprehensive design framework addressing both hardware and software aspects of the BTE-EEG emotion recognition system for data acquisition and monitoring. By utilizing BTE-EEG technology, this study aims to improve comfort, mobility, and signal quality compared to traditional scalp EEGs, thus enabling continuous monitoring in real-world settings. Furthermore, the integration of quantum computing and machine learning represents a paradigm shift in emotion recognition and cognitive tasks, offering exponential speedups and superior pattern recognition capabilities compared to classical approaches. The findings underscore the efficacy of the proposed BTE EEG system in accurately detecting emotional states (positive and negative). This advancement shows promise for enhancing our understanding of emotional disorders and improving clinical interventions tailored to individual needs.