The electroencephalogram (EEG) has been the gold standard for quantifying mental workload; however, due to its complexity and non-portability, it can be constraining. ECG signals, which are feasible on wearable equipment pieces such as headbands, present a promising method for cognitive state monitoring. This research explores whether electrocardiogram (ECG) signals are able to indicate mental workload consistently and act as surrogates for EEG-based cognitive indicators. This study investigates whether ECG-derived features can serve as surrogate indicators of cognitive load, a concept traditionally quantified using EEG. Using a publicly available multimodal dataset (OpenNeuro) of EEG and ECG recorded during working-memory and listening tasks, features of HRV and Catch22 descriptors are extracted from ECG, and spectral band-power with Catch22 features from EEG. A cross-modal regression framework based on XGBoost was trained to map ECG-derived HRV representations to EEG-derived cognitive features. In order to address data sparsity and model brain-heart interactions, we integrated the PSV-SDG to produce EEG-conditioned synthetic HRV time series.This addresses the challenge of inferring cognitive load solely from ECG-derived features using a combination of multimodal learning, signal processing, and synthetic data generation. These outcomes form a basis for light, interpretable machine learning models that are implemented through wearable biosensors in non-lab environments. Synthetic HRV inclusion enhances robustness, particularly in sparse data situations. Overall, this work is an initiation for building low-cost, explainable, and real-time cognitive monitoring systems for mental health, education, and human-computer interaction, with a focus on ageing and clinical populations.

翻译：脑电图（EEG）一直是量化心理工作负荷的金标准，但其复杂性和非便携性可能带来限制。心电图（ECG）信号在头带等可穿戴设备上易于采集，为认知状态监测提供了一种前景广阔的方法。本研究探讨心电图信号是否能够稳定指示心理工作负荷，并作为基于脑电的认知指标的替代指标。研究利用公开的多模态数据集（OpenNeuro），该数据集记录了工作记忆与听觉任务期间的脑电和心电信号，从心电信号中提取心率变异性特征与Catch22描述符，从脑电信号中提取频段功率及Catch22特征。基于XGBoost构建跨模态回归框架，将心电衍生的心率变异性表征映射至脑电衍生的认知特征。为应对数据稀疏性并建模大脑-心脏交互，本研究集成PSV-SDG方法生成脑电条件化的合成心率变异性时间序列。该方法通过融合多模态学习、信号处理与合成数据生成技术，解决了仅依靠心电特征推断认知负荷的挑战。研究成果为在非实验室环境下通过可穿戴生物传感器实现轻量化、可解释的机器学习模型奠定了基础。合成心率变异性数据的引入增强了模型鲁棒性，尤其在数据稀疏场景下表现显著。总体而言，本研究为构建面向心理健康、教育及人机交互领域的低成本、可解释、实时认知监测系统提供了初步框架，尤其关注老年与临床人群的应用需求。