S S D Maha Lakshmi , Mohd Akbar 2 1 ( January - March ) The classification methods based on EEG have been shown to be valuable for EEG diagnosis of neurological diseases and braincomputer interface BCI development However too often these types of approaches to deeper learning and machine learning for quantum computing are not interpretable and pose challenges for clinical data integration For medical practitioners there needs to be clear clinically useful explanations before a diagnostic prediction is made automatically For Sintering a Quantum EEG classification pipeline with an Explainable Artificial Intelligence XAI could be beneficial as shown in this paper The proposed framework attempts to make the framework simultaneously predictable while treating it as an interpretable one by multiband signal preprocessing data the variational quantum feature would be embedded in the attentionguiding classification and the posthoc of the modules would be explained The system in particular utilizes Shapley Additive explanations SHAP feature explanation together with multihead attention quantum kernel functions to deliver mathematically reliable and clinically sensible explanations The results of standard test experiments with EEG data indicate that the network proposed in the work achieves a type accuracy of 984 and an interpretability score of 093 which is significantly higher than the classification accuracy of the traditional CNN network the Transformer network and the Quantum SVM network The results demonstrate that the proposed system can bring the quantum advantage closer to being explainable and more trusted clinical Artificial Intelligence Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026