Automated sleep staging is a fundamental application of passive Brain-Computer Interfaces (pBCI), decoding spontaneous neural states to enable closed-loop interventions independent of user intent. This study evaluates criticality features derived from Detrended Fluctuation Analysis (DFA) for the specific identification of deep sleep (N3). We analyzed $347,232$ EEG epochs from $290$ older women using UMAP manifold learning to visualize state transitions. Subsequently, six classifiers were benchmarked via 10-fold cross-validation, using balanced accuracy to determine the optimal "state-sensing" engine for neurofeedback.Naive Bayes achieved the highest mean balanced accuracy ($87.17\% \pm 0.24\%$), significantly outperforming a fully connected deep neural network (FNN: $81.58\%$) and Random Forest ($80.97\%$). Linear models (LDA: $57.21\%$; SVM: $51.01\%$) performed poorly, indicating that DFA-derived criticality features reside on a distinct, non-linear manifold. Probabilistic decoding of EEG criticality provides a high-accuracy sensing mechanism for pBCIs. This robust classification pipeline supports the development of state-dependent neurofeedback, such as targeted auditory stimulation, to enhance cognitive recovery.

翻译：自动睡眠分期是被动脑机接口（pBCI）的基础应用，通过解码自发性神经状态，在无需用户意图的情况下实现闭环干预。本研究评估了基于去趋势波动分析（DFA）的临界性特征在特定识别深度睡眠（N3）中的效果。我们利用UMAP流形学习分析了来自290名老年女性的347,232个脑电周期，以可视化状态转换。随后，通过10折交叉验证对六种分类器进行基准测试，使用平衡准确率确定用于神经反馈的最优“状态感知”引擎。朴素贝叶斯获得了最高的平均平衡准确率（87.17% ± 0.24%），显著优于全连接深度神经网络（FNN：81.58%）和随机森林（80.97%）。线性模型（LDA：57.21%；SVM：51.01%）表现不佳，表明DFA衍生的临界性特征位于一个独特的非线性流形上。脑电信号临界性的概率解码为pBCI提供了高准确度的感知机制。这一稳健的分类流程支持了状态依赖性神经反馈（如靶向听觉刺激）的发展，以促进认知恢复。