用可穿戴心电图监测认知负荷，替代难携带的脑电设备。
Unveiling the Heart-Brain Connection: An Analysis of ECG in Cognitive Performance
- 用心电图的时域与统计特征，映射到脑电表征的认知空间。
- 心电投影能准确区分不同认知状态，分类效果接近脑电。
- 适合需要实时、便携认知监测的场景，如智能穿戴应用。
理解认知活动中神经与心脏系统的交互对推动生理计算至关重要。尽管脑电图（EEG）是评估心理负荷的金标准，但其便携性差限制了实际应用。通过可穿戴设备广泛获取的心电图（ECG）提供了更实用的替代方案。本研究探讨了ECG信号是否能可靠反映认知负荷，并作为基于EEG的指标代理。我们采集了两种范式下（工作记忆任务和被动听觉任务）的多模态数据。针对每种模态，分别提取了ECG的时域心率变异性（HRV）指标与Catch22特征，以及EEG的频谱特征与Catch22特征。提出一种跨模态XGBoost框架，将ECG特征投影至代表认知状态的EEG空间，从而仅凭ECG实现负荷推断。结果表明，基于ECG的投影能有效捕捉认知状态变化，为准确分类提供有力支持。研究证实ECG是一种可解释、实时、可穿戴的日常认知监测解决方案。
原文摘要 · Abstract (English)
Understanding the interaction of neural and cardiac systems during cognitive activity is critical to advancing physiological computing. Although EEG has been the gold standard for assessing mental workload, its limited portability restricts its real-world use. Widely available ECG through wearable devices proposes a pragmatic alternative. This research investigates whether ECG signals can reliably reflect cognitive load and serve as proxies for EEG-based indicators. In this work, we present multimodal data acquired from two different paradigms involving working-memory and passive-listening tasks. For each modality, we extracted ECG time-domain HRV metrics and Catch22 descriptors against EEG spectral and Catch22 features, respectively. We propose a cross-modal XGBoost framework to project the ECG features onto EEG-representative cognitive spaces, thereby allowing workload inferences using only ECG. Our results show that ECG-derived projections expressively capture variation in cognitive states and provide good support for accurate classification. Our findings underpin ECG as an interpretable, real-time, wearable solution for everyday cognitive monitoring.
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