Yan Tong
Research Mentor: Emily Diamond
Mentor Department: Psychology, LSA
Author(s): Emily Diamond, Yan Tong
Session: Session 6 (3:00 PM – 3:50 PM)
Presentation Type: Poster 75
Abstract
Electrocardiogram (ECG) signals offer a valuable window into physiological activity during dynamic social experiences. However, raw ECG recordings are often affected by motion artifacts, signal noise, and inconsistencies in data collection, making careful preprocessing essential for accurate analysis. This project focused on improving ECG data quality through systematic cleaning and preprocessing procedures. Raw signals were visually inspected to identify artifacts and irregularities, followed by the application of standardized filtering approaches to enhance signal clarity. R-peaks were detected and manually verified when needed to ensure reliable extraction of interbeat intervals (IBIs). In addition, physiological datasets were organized and preprocessing workflows were documented to promote consistency and reproducibility across participants. High-quality ECG data support more accurate examination of cardiac responses in a range of psychological and behavioral research contexts. In particular, cleaned ECG signals can be used to investigate physiological patterns associated with emotional regulation, social interaction, and interpersonal connection. By strengthening the reliability of physiological measures, this work contributes to broader efforts to understand how bodily processes relate to human relationships and real-time social experiences.
