This study presents QuanvNeXt, an end-to-end fully quanvolutional model for EEG-based depression diagnosis. QuanvNeXt incorporates a novel Cross Residual block, which reduces feature homogeneity and strengthens cross-feature relationships while retaining parameter efficiency. We evaluated QuanvNeXt on two open-source datasets, where it achieved an average accuracy of 93.1% and an average AUC-ROC of 97.2%, outperforming state-of-the-art baselines such as InceptionTime (91.7% accuracy, 95.9% AUC-ROC). An uncertainty analysis across Gaussian noise levels demonstrated well-calibrated predictions, with ECE scores remaining low (0.0436, Dataset 1) to moderate (0.1159, Dataset 2) even at the highest perturbation (ε = 0.1). Additionally, a post-hoc explainable AI analysis confirmed that QuanvNeXt effectively identifies and learns spectrotemporal patterns that distinguish between healthy controls and major depressive disorder. Overall, QuanvNeXt establishes an efficient and reliable approach for EEG-based depression diagnosis.

翻译：本研究提出了QuanvNeXt，一种用于基于脑电图（EEG）的抑郁症诊断的端到端全量子卷积模型。QuanvNeXt引入了一种新颖的交叉残差块，该块在保持参数效率的同时，减少了特征同质性并增强了跨特征关系。我们在两个开源数据集上评估了QuanvNeXt，其平均准确率达到93.1%，平均AUC-ROC达到97.2%，优于最先进的基线模型，如InceptionTime（准确率91.7%，AUC-ROC 95.9%）。在不同高斯噪声水平下的不确定性分析表明，其预测校准良好，即使在最高扰动水平（ε = 0.1）下，ECE分数也保持在较低（数据集1：0.0436）到中等（数据集2：0.1159）的范围。此外，事后可解释性人工智能分析证实，QuanvNeXt能够有效识别并学习区分健康对照组与重度抑郁障碍患者的频谱-时间模式。总体而言，QuanvNeXt为基于脑电图的抑郁症诊断建立了一种高效且可靠的方法。