Unveiling the Heart-Brain Connection: An Analysis of ECG in Cognitive Performance
This work addresses the need for portable, real-time cognitive monitoring solutions, offering an incremental improvement by leveraging widely available ECG data instead of less portable EEG.
The study tackled the problem of using ECG signals from wearable devices as a reliable proxy for EEG-based cognitive load assessment, showing that ECG-derived projections can capture cognitive state variations and support accurate classification.
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.