用脑电波无感检测老人认知衰退，避免人为误差。
Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version
- 用轻量级CNN分析被动视觉刺激下的脑电信号
- 无需配合任务即可准确判断认知受损程度
- 适合老年人、测试不依赖注意力和教育背景
早期痴呆诊断需敏感反映结构与功能变化的生物标志物。尽管结构性神经影像进展显著，但客观的功能性生物标志物在早期认知衰退检测中仍严重缺失。当前认知评估多依赖行为反应，易受努力程度、练习效应和教育背景影响，阻碍早期精准识别。本文提出一种新方法：利用轻量级卷积神经网络（CNN）直接从脑电图（EEG）数据推断认知损伤水平。关键在于采用被动快速周期性视觉刺激（FPVS）范式，无需参与者主动响应或理解任务。该被动方式提供独立于干扰因素的工作记忆客观度量，为早期、无偏倚的认知衰退检测提供了新路径。
原文摘要 · Abstract (English)
Early dementia diagnosis requires biomarkers sensitive to both structural and functional brain changes. While structural neuroimaging biomarkers have progressed significantly, objective functional biomarkers of early cognitive decline remain a critical unmet need. Current cognitive assessments often rely on behavioral responses, making them susceptible to factors like effort, practice effects, and educational background, thereby hindering early and accurate detection. This work introduces a novel approach, leveraging a lightweight convolutional neural network (CNN) to infer cognitive impairment levels directly from electroencephalography (EEG) data. Critically, this method employs a passive fast periodic visual stimulation (FPVS) paradigm, eliminating the need for explicit behavioral responses or task comprehension from the participant. This passive approach provides an objective measure of working memory function, independent of confounding factors inherent in active cognitive tasks, and offers a promising new avenue for early and unbiased detection of cognitive decline.
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