针对脑电数据缺失问题，提出新方法提升抑郁症检测准确率
Incomplete Depression Feature Selection with Missing EEG Channels
- 融合缺失电极信息与自适应加权，改进正交回归建模
- 在3/64/128通道设置下，优于10种主流特征选择方法
- 适合实际脑电采集中存在电极脱落场景的抑郁分析
抑郁症是严重影响身心健康的重大精神障碍。基于脑电图（EEG）的抑郁症分析近年取得进展，但其特征常包含冗余、无关和噪声信息。真实采集中常因电极脱落或强噪声干扰导致数据缺失。为此，我们提出一种新型特征选择方法——不完整抑郁症特征选择（IDFS-MEC），将缺失电极标识信息与自适应通道加权学习融入正交回归，降低不完整通道对模型的影响，并通过全局冗余最小化学习减少所选特征子集间的冗余性。在MODMA和PRED-d003数据集上的大量实验表明，在3、64和128通道设置下，IDFS-MEC选取的脑电特征子集性能均优于10种主流特征选择方法。
原文摘要 · Abstract (English)
As a critical mental health disorder, depression has severe effects on both human physical and mental well-being. Recent developments in EEG-based depression analysis have shown promise in improving depression detection accuracies. However, EEG features often contain redundant, irrelevant, and noisy information. Additionally, real-world EEG data acquisition frequently faces challenges, such as data loss from electrode detachment and heavy noise interference. To tackle the challenges, we propose a novel feature selection approach for robust depression analysis, called Incomplete Depression Feature Selection with Missing EEG Channels (IDFS-MEC). IDFS-MEC integrates missing-channel indicator information and adaptive channel weighting learning into orthogonal regression to lessen the effects of incomplete channels on model construction, and then utilizes global redundancy minimization learning to reduce redundant information among selected feature subsets. Extensive experiments conducted on MODMA and PRED-d003 datasets reveal that the EEG feature subsets chosen by IDFS-MEC have superior performance than 10 popular feature selection methods among 3-, 64-, and 128-channel settings.
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