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.

翻译：理解认知活动期间神经与心脏系统的交互作用对于推进生理计算至关重要。尽管脑电图（EEG）一直是评估心理负荷的金标准，但其有限的便携性限制了其在现实世界中的应用。通过可穿戴设备广泛获取的心电图（ECG）提供了一种实用的替代方案。本研究探讨了ECG信号是否能可靠地反映认知负荷，并作为基于EEG指标的代理。在本工作中，我们展示了从涉及工作记忆和被动聆听任务的两种不同范式获取的多模态数据。针对每种模态，我们分别提取了ECG时域心率变异性（HRV）指标和Catch22描述符，并与EEG频谱特征及Catch22特征进行对比。我们提出了一种跨模态XGBoost框架，将ECG特征映射到具有EEG代表性的认知空间，从而仅使用ECG即可进行负荷推断。我们的结果表明，ECG衍生的投影能有效捕捉认知状态的变化，并为准确分类提供了良好支持。我们的发现证实了ECG作为一种可解释、实时、可穿戴的日常认知监测解决方案的潜力。