基于梯度记忆池与信息熵的脑电特征选择方法，提升神经疾病诊断准确率。
IEFS-GMB: Gradient Memory Bank-Guided Feature Selection Based on Information Entropy for EEG Classification of Neurological Disorders
- 用历史梯度构建动态记忆库，结合信息熵评估特征重要性
- 在4个公开数据集上使分类准确率提升0.64%~6.45%
- 增强模型可解释性，适合临床神经疾病辅助诊断场景
基于深度学习的脑电图（EEG）分类对神经系统疾病的自动化检测至关重要，有助于提高诊断精度并实现早期干预。然而，EEG信号信噪比低，限制了模型性能，因此特征选择（FS）对优化神经网络编码器所学表征尤为关键。现有特征选择方法大多未专为EEG诊断设计，多依赖特定架构且缺乏可解释性，应用受限；且多数仅使用单次迭代数据，鲁棒性不足。为此，本文提出IEFS-GMB：一种基于信息熵、由梯度记忆池引导的特征选择方法。该方法构建动态记忆库存储历史梯度，通过信息熵计算特征重要性，并以熵值加权筛选有效EEG特征。在四个公开神经疾病数据集上的实验表明，集成IEFS-GMB的编码器相比基线模型准确率提升0.64%至6.45%。该方法优于四种对比的特征选择技术，同时提升了模型可解释性，支持其在临床环境中的实际应用。
原文摘要 · Abstract (English)
Deep learning-based EEG classification is crucial for the automated detection of neurological disorders, improving diagnostic accuracy and enabling early intervention. However, the low signal-to-noise ratio of EEG signals limits model performance, making feature selection (FS) vital for optimizing representations learned by neural network encoders. Existing FS methods are seldom designed specifically for EEG diagnosis; many are architecture-dependent and lack interpretability, limiting their applicability. Moreover, most rely on single-iteration data, resulting in limited robustness to variability. To address these issues, we propose IEFS-GMB, an Information Entropy-based Feature Selection method guided by a Gradient Memory Bank. This approach constructs a dynamic memory bank storing historical gradients, computes feature importance via information entropy, and applies entropy-based weighting to select informative EEG features. Experiments on four public neurological disease datasets show that encoders enhanced with IEFS-GMB achieve accuracy improvements of 0.64% to 6.45% over baseline models. The method also outperforms four competing FS techniques and improves model interpretability, supporting its practical use in clinical settings.
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