A new approach for the quantification of synchrony of multivariate non-stationary psychophysiological variables during emotion eliciting stimuli

Abstract

Emotion eliciting situations are accompanied by changes of multiple variables associated with subjective, physiological and behavioral responses. The quantification of the overall simultaneous synchrony of psychophysiological reactions plays a major role in emotion theories and has received increased attention in recent years. From a psychometric perspective, the reactions represent multivariate non-stationary intra-individual time series. In this paper, a new time-frequency based latent variable approach for the quantification of the synchrony of the responses is presented. The approach is applied to empirical data, collected during an emotion eliciting situation. The results are compared with a complementary inter-individual approach of Hsieh et al. (2011). Finally, the proposed approach is discussed in the context of emotion theories, and possible future applications and limitations are provided.

Keywords: coherence; emotion regulation; frequency; latent variables; multivariate; psychophysiology; synchrony; time series.

Figure 1

Example of two non-stationary signals in time, frequency and time-frequency domains.

Figure 2

Spectral coherence and time-frequency coherence of the simulated non-stationary signals.

Figure 3

The implementation of the Gaussian smoothing kernel. Smoothing of the time-frequency distributions is necessary for C_XY(t, f) to be well defined and produce meaningful results.

Figure 4

Example of ECG, EDA and respiration signals in time, frequency and time-frequency domains.

Figure 5

Example of synchronously measured EDA and respiration signals that both contain a cyclic component at the frequency region of approx. 0.4 Hz.

Figure 6

Element-wise logarithm of the time-frequency distribution of the EDA signal depicted in Figure 4, second panel. For this example, the pulse frequency region and its harmonics become clearly visible in the EDA signal.

Figure 7

Example of coherences between ECG, EDA, and respiration signals in the time-frequency domain. The first panel shows the coherence between the ECG and EDA signals, the second panel shows the coherence between the ECG and respiration signals, and the third panel shows the coherence between the EDA and respiration signals.

Figure 8

Example of coherences within delineated regions, i.e., the pulse and respiration regions, between ECG and respiration signals.

Figure 9

Example of the pairwise coherence measure based on the concept of delineated regions in the time-frequency plane as shown in Figure 8. Series 1 & 2 plots the coherence of the ECG and EDA signals in the pulse region (solid) and EDA region (dashed), respectively. Series 3 & 4 depict the coherence of the respiration and EDA signals in the respiration region (solid) and EDA region (dashed). Series 5 & 6 depict the coherence of the respiration and ECG signals in the pulse region (solid) and respiration region (dashed).

Figure 10

Example of an overall synchrony measure while a participant was watching a neutral picture and a disgust eliciting picture for 10 s each.

Figure 11

Example of six time-frequency based pairwise coherence measures (series) for one individual watching funny film clips. Series 1: ECG-EDA coherence (EDA region), Series 2: ECG-EDA coherence (ECG region), Series 3: RE-ECG coherence (ECG region), Series 4: RE-ECG coherence (RE region), Series 5: RE-EDA coherence (EDA region), Series 6: RE-EDA coherence (RE region). During the first 60 s, the participant was watching a film clip, which was rated as moderately funny. During the last 60 s, the participant was shown a funnier film clip.

Figure 12

Example for two overall synchrony measures for two participants. During the first 60 s, the participants were watching a film clip, which was rated as moderately funny. During the last 60 s, the participants were shown a funnier film clip.