Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals

Abstract

Subjective experience and physiological activity are fundamental components of emotion. There is an increasing interest in the link between experiential and physiological processes across different disciplines, e.g., psychology, economics, or computer science. However, the findings largely rely on sample sizes that have been modest at best (limiting the statistical power) and capture only some concurrent biosignals. We present a novel publicly available dataset of psychophysiological responses to positive and negative emotions that offers some improvement over other databases. This database involves recordings of 1157 cases from healthy individuals (895 individuals participated in a single session and 122 individuals in several sessions), collected across seven studies, a continuous record of self-reported affect along with several biosignals (electrocardiogram, impedance cardiogram, electrodermal activity, hemodynamic measures, e.g., blood pressure, respiration trace, and skin temperature). We experimentally elicited a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. Psychophysiology of positive and negative emotions (POPANE) database is a large and comprehensive psychophysiological dataset on elicited emotions.

Fig. 1

A schematic visualization of the data acquisition procedure. Panel a presents the approximate placement of the sensors. Panel b presents hardware (in white) and software (in grey) used for data acquisition. This figure was created by Katarzyna Janicka. The copyright of the figure is held by Katarzyna Janicka.

Fig. 2

Schematic presentation of data preprocessing. The data were first exported from the acquisition software (panel a) and then preprocessed and integrated into CSV files. The resulting CSV files can be easily loaded into most statistical software and packages, such as IBM’s SPSS or Python Pandas & SciPy modules (for visualization in Python’s Pandas module, see panel b).

Fig. 3

Data histograms of baseline psychophysiological levels. This figure presents the distribution of the mean psychophysiological levels for resting baseline but does not present raw recordings (e.g., ECG in mV) that require further processing (e.g., analysis to calculate HR or HRV).