Unveiling the Heart-Brain Connection: An Analysis of ECG in Cognitive Performance

2026-01-04

arXiv.org

AI Summary

This study investigates whether electrocardiogram (ECG) signals can serve as a viable alternative to electroencephalography (EEG) for real-time, wearable cognitive load monitoring. By simultaneously acquiring multimodal physiological data during working memory and passive auditory tasks, the authors extract time-domain heart rate variability (HRV) and Catch22 time-series features from ECG signals and develop a cross-modal XGBoost mapping framework to project these ECG-derived features into the EEG-based cognitive representational space. This work presents the first systematic validation of ECG’s proxy capability in cognitive load assessment, demonstrating that ECG-derived features effectively capture dynamic changes in cognitive states and achieve strong performance in classification tasks. The findings establish a novel, interpretable, real-time, and practical paradigm for physiological computing in everyday wearable applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Hardware-aware ML

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• ECG
• cognitive load
• EEG
• physiological computing
• wearable devices

Innovation

• ECG
• cognitive load
• cross-modal learning
• XGBoost
• physiological computing