Evolving Explainable Artificial Intelligence for electroencephalography-based mental health classification in digital twin systems

Abstract

The classification of mental health conditions using electroencephalogram (EEG) signals has gained increasing attention due to its non-invasive nature and potential for early diagnosis. Explainable Artificial Intelligence (XAI) plays a crucial role in enhancing the interpretability of machine learning models; however, traditional XAI methods often suffer from high computational costs and redundant feature selection. In this study, we propose Envolving Explainable Artificial Intelligence (E-XAI), an evolutionary XAI framework that leverages Genetic Algorithms (GA) to efficiently search for the optimal EEG feature subset, reducing computational overhead while maintaining interpretability. Furthermore, this work integrates Digital Twin technology, enabling a dynamic and adaptive representation of EEG-based mental states. The proposed framework allows real-time monitoring, remote diagnosis, and personalized mental health interventions by continuously updating the digital twin model with real-time EEG data. This enhances model adaptability, robustness, and scalability for mental health classification. Experimental results on a benchmark EEG dataset demonstrate that E-XAI with Digital Twin technology significantly reduces the computational time of XAI techniques while improving the classification performance and interpretability of mental health classification systems. This advancement provides a promising pathway for real-time, scalable, and intelligent EEG-based mental health analysis.