Collaborative explainable ai for EEG mental health monitoring with constrained QA-Tuned LLM alignment

Abstract

The monitoring of mental health states using electroencephalogram (EEG) signals has gained increasing attention due to its non-invasive nature for psychological disorders. Large Language Models (LLMs) and Explainable Artificial Intelligence (XAI) have been utilized in advancing the intelligence and interpretability of EEG analysis. However, existing methods face critical bottlenecks, including the fundamental modal gap, high computational costs, and poor global consistency. The limitation of rigid classification tasks without supporting clinical reasoning and natural language interaction. In this study, we propose a collaborative explainable AI framework for EEG mental health monitoring with constrained question-and-answer (QA) tuned LLM alignment, which builds a smooth transformation path from raw EEG signals to evidence, and constructs a structured QA dataset for the instruction fine-tuning of LLMs. The central objective of this work is not simply to maximize EEG classification accuracy, but to develop an evidence-grounded alignment and explanation framework that connects EEG-derived physiological evidence with QA-based LLM reasoning. Furthermore, this work designs a transparent collaborative XAI mechanism that embeds interpretable EEG feature information as prior knowledge directly into the QA generation process of the LLM, and develops a multi-level interpretable pipeline combining attention heatmap analysis and decision tree surrogate modeling to achieve precise alignment between LLM internal reasoning and EEG neurophysiological patterns. The proposed framework addresses the limitations of traditional rigid EEG classification tasks, promotes the XAI paradigm shift from high-cost post-hoc explanations to transparent embedded explanations, and enables robust clinical reasoning and natural language interaction based on EEG signals. Experimental results on a benchmark EEG mental state dataset demonstrate that the proposed framework stably captures neurophysiological characteristics corresponding to different mental states, and effectively improves decision transparency and clinical credibility of EEG-based mental health monitoring systems. In this setting, classification performance is treated as one evaluation aspect, while the primary contribution lies in constrained evidence-grounded alignment and QA-based LLM explainability. This advancement provides an initial feasibility study of real-time, scalable, and trustworthy intelligent EEG-based mental health analysis.