Exploring Affective Peripheral Patterns Based on Body Surface Potentials With Covariance

Affective patterns based on physiological signals reflect bodily changes linked to specific emotional states. Previous studies on the cardiac electrical signal, a key peripheral physiological signal, were limited by the measurement density of single-lead ECG signal, focusing solely on temporal pattern analysis but ignoring topographic pattern analysis that can reflect the body's emotional response. Our research advances affective peripheral pattern studies by innovatively using body surface potentials to comprehensively monitor cardiac electrical activity with increased measurement density. To tackle the challenge of extracting spatial and temporal features from multi-channel body surface potentials, we establish a dynamic correlation among these diverse channel signals through covariance matrices. Our hypothesis is that the dynamic inter-channel relationship provides a valuable source of insights into emotional clues. Experimental results demonstrate that the extracted spatial and temporal features effectively capture topographic and temporal patterns from cardiac electrical signals, and achieve excellent performance in classification tasks simultaneously. Our finding reveals affective patterns based on body surface potentials for the first time, offering novel insights into affective peripheral patterns analysis.