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

TL;DR

This study demonstrates that ECG signals can reliably reflect cognitive load and serve as practical, wearable alternatives to EEG for real-time cognitive monitoring, using a novel cross-modal machine learning approach.

Contribution

The paper introduces a cross-modal XGBoost framework that projects ECG features onto EEG-like cognitive spaces, enabling accurate workload inference from ECG alone.

Findings

ECG features can distinguish different cognitive states effectively.

The proposed method achieves high classification accuracy in workload detection.

ECG-based monitoring is feasible for real-world, wearable cognitive assessment.

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.