Wearable technologies have transformed health monitoring by enabling the continuous assessment and analysis of physiological signals in everyday settings. This research aims to compare two widely used methods, electrocardiography (ECG) and photoplethysmography (PPG), for their effectiveness in monitoring and measuring Heartbeat Evoked Potentials (HEPs), which represent cortical brain activity time-locked to cardiac events.
HEPs are neural signals derived using Electroencephalography (EEG), a technique for recording brain activity. Precise synchronization of EEG data with cardiac events is required for extracting HEPs from neural activity. ECG captures the heart’s electrical activity, providing highly accurate and precise markers, such as the R-wave, which is commonly used to segment EEG data into cardiac epochs for HEP analysis.
PPG is an optical method that measures blood volume changes in peripheral vessels. It detects the expansion and relaxation of blood vessels through light transmission and reflection, offering a non-invasive alternative. However, PPG introduces delays due to pulse transit time and is more susceptible to motion artifacts, potentially affecting its suitability for extraction of HEP data.
This study will involve 20 participants, where ECG and PPG signals will be simultaneously recorded alongside EEG during rest and activity. The research will evaluate signal timing, accuracy, and artifact susceptibility to determine the feasibility of using PPG-derived cardiac markers for EEG segmentation. Comparisons will focus on the reliability of HEPs extracted from ECG-aligned versus PPG-aligned data.
The findings aim to provide new insights into the strengths and limitations of ECG and PPG for time-sensitive neural engineering applications, advancing wearable technology’s potential for studying brain-body interactions and informing the design of future devices for clinical and research use.