Physiological Synchrony in EEG, Electrodermal Activity and Heart Rate Detects Attentionally Relevant Events in Time

Abstract

Interpersonal physiological synchrony (PS), or the similarity of physiological signals between individuals over time, may be used to detect attentionally engaging moments in time. We here investigated whether PS in the electroencephalogram (EEG), electrodermal activity (EDA), heart rate and a multimodal metric signals the occurrence of attentionally relevant events in time in two groups of participants. Both groups were presented with the same auditory stimulus, but were instructed to attend either to the narrative of an audiobook (audiobook-attending: AA group) or to interspersed emotional sounds and beeps (stimulus-attending: SA group). We hypothesized that emotional sounds could be detected in both groups as they are expected to draw attention involuntarily, in a bottom-up fashion. Indeed, we found this to be the case for PS in EDA or the multimodal metric. Beeps, that are expected to be only relevant due to specific “top-down” attentional instructions, could indeed only be detected using PS among SA participants, for EDA, EEG and the multimodal metric. We further hypothesized that moments in the audiobook accompanied by high PS in either EEG, EDA, heart rate or the multimodal metric for AA participants would be rated as more engaging by an independent group of participants compared to moments corresponding to low PS. This hypothesis was not supported. Our results show that PS can support the detection of attentionally engaging events over time. Currently, the relation between PS and engagement is only established for well-defined, interspersed stimuli, whereas the relation between PS and a more abstract self-reported metric of engagement over time has not been established. As the relation between PS and engagement is dependent on event type and physiological measure, we suggest to choose a measure matching with the stimulus of interest. When the stimulus type is unknown, a multimodal metric is most robust.

Multimodal Physiological Synchrony as Measure of Attentional Engagement

“…The first experiment has been conducted and the first papers, number one and two in Table 1, have been published [? ] and submitted [35], respectively. In paper one, we monitored EEG, EDA and heart rate across 26 participants that were either instructed to focus their attention on a 66 minute audiobook (𝑁 = 13) or on interspersed emotional sounds and beeps they had to keep track of (𝑁 = 13).…”

Interpersonal Physiological Synchrony for Detecting Moments of Connection in Persons With Dementia: A Pilot Study

“…We compare the performance of NSTE asymmetry to a more traditional measure of physiological synchrony: the single session index. Introduced by Marci and Orr ( 2006 ) with several more recent modifications (Slovák et al, 2014 ; Karvonen et al, 2016 ; Haataja et al, 2018 ; Malmberg et al, 2019 ; Misal et al, 2020 ; Stuldreher et al, 2020 ), the SSI has been well-established with electrodermal activity signals. Previous work has demonstrated that high SSI measured from EDA signals between individuals have been associated with high levels of dyadic and group emotional engagement.…”

“…Individuals have different dominant physiological modalities and may manifest synchrony in other biosignals (e.g., heart rate) (Lai Kwan et al, 2019). Other studies have shown that performance of a multimodal physiological synchrony measure was more robust than a single physiological metric across different types of stimuli, even though the maximum performance did not improve over a single physiological metric (Verma and Tiwary, 2014;Stuldreher et al, 2020).…”

Integrating Cognitive Developmental Neuroscience in Society: Lessons Learned From a Multidisciplinary Research Project on Education and Social Safety of Youth

“…While physiological synchrony in EEG has been demonstrated in the classroom ( Dikker et al, 2017 ), easier, less obtrusive collection and processing of data would make interpersonal physiological synchrony a more suitable measure for attention in the classroom. Therefore, within the NeurolabNL Startimpulse project, we determined whether other physiological measures, namely heart rate and skin conductance, provide similar insights into attentional allocation as EEG ( Stuldreher et al, 2020a , b ). In this study, participants listened to the same audiobook, mixed with other sounds.…”