Zhu Lixian, Wang Rui, Jin Xiaokun, Li Yuwen, Tian Fuze, Cai Ran, Qian Kun, Hu Xiping, Hu Bin, Yamamoto Yoshiharu, Schuller Bjorn W
IEEE Trans Neural Syst Rehabil Eng. 2025;33:1411-1426. doi: 10.1109/TNSRE.2025.3557275. Epub 2025 Apr 18.
With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have been widely proposed. However, existing studies have mostly focused on the accuracy of depression recognition, ignoring the association between features and models. Additionally, there is a lack of research on the contribution of different features to depression recognition. To this end, this study introduces an innovative approach to depression detection using EEG data, integrating Ant-Lion Optimization (ALO) and Multi-Agent Reinforcement Learning (MARL) for feature fusion analysis. The inclusion of Explainable Artificial Intelligence (XAI) methods enhances the explainability of the model's features. The Time-Delay Embedded Hidden Markov Model (TDE-HMM) is employed to infer internal brain states during depression, triggered by audio stimulation. The ALO-MARL algorithm, combined with hyper-parameter optimization of the XGBoost classifier, achieves high accuracy (93.69%), sensitivity (88.60%), specificity (97.08%), and F1-score (91.82%) on a auditory stimulus-evoked three-channel EEG dataset. The results suggest that this approach outperforms state-of-the-art feature selection methods for depression recognition on this dataset, and XAI elucidates the critical impact of the minimum value of Power Spectral Density (PSD), Sample Entropy (SampEn), and Rényi Entropy (Ren) on depression recognition. The study also explores dynamic brain state transitions revealed by audio stimuli, providing insights for the clinical application of AI algorithms in depression recognition.
随着情感计算和人工智能（AI）技术的发展，基于脑电图（EEG）的抑郁症检测方法已被广泛提出。然而，现有研究大多集中在抑郁症识别的准确性上，而忽略了特征与模型之间的关联。此外，对于不同特征对抑郁症识别的贡献缺乏研究。为此，本研究引入了一种利用EEG数据进行抑郁症检测的创新方法，将蚁狮优化（ALO）和多智能体强化学习（MARL）集成用于特征融合分析。可解释人工智能（XAI）方法的加入增强了模型特征的可解释性。采用时延嵌入隐马尔可夫模型（TDE-HMM）来推断由音频刺激引发的抑郁症期间的大脑内部状态。ALO-MARL算法与XGBoost分类器的超参数优化相结合，在一个听觉刺激诱发的三通道EEG数据集上实现了高精度（93.69%）、灵敏度（88.60%）、特异性（97.08%）和F1分数（91.82%）。结果表明，该方法在此数据集上的抑郁症识别性能优于当前最先进的特征选择方法，并且XAI阐明了功率谱密度（PSD）最小值、样本熵（SampEn）和雷尼熵（Ren）对抑郁症识别的关键影响。该研究还探索了音频刺激揭示的动态大脑状态转变，为AI算法在抑郁症识别中的临床应用提供了见解。
