Objective. Concurrent changes in physiological signals across multiple listeners (physiological synchrony—PS), as caused by shared affective or cognitive processes, may be a suitable marker of selective attentional focus. We aimed to identify the selective attention of participants based on PS with individuals sharing attention with respect to different stimulus aspects. Approach. We determined PS in electroencephalography (EEG), electrodermal activity (EDA) and electrocardiographic inter-beat interval (IBI) of participants who all heard the exact same audio track, but were instructed to either attend to the audiobook or to interspersed auditory events such as affective sounds and beeps that attending participants needed to keep track of. Main results. PS in all three measures reflected the selective attentional focus of participants. In EEG and EDA, PS was higher for participants when linked to participants with the same attentional instructions than when linked to participants instructed to focus on different stimulus aspects, but in IBI this effect did not reach significance. Comparing PS between a participant and members from the same or the different attentional group allowed for the correct identification of the participant’s attentional instruction in 96%, 73% and 73% of the cases, for EEG, EDA and IBI, respectively, all well above chance level. PS with respect to the attentional groups also predicted performance on post-audio questions about the groups’ stimulus content. Significance. Our results show that selective attention of participants can be monitored using PS, not only in EEG, but also in EDA and IBI. These results are promising for real-world applications, where wearables measuring peripheral signals like EDA and IBI may be preferred over EEG sensors.

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.

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.

Measuring concurrent changes in autonomic physiological responses aggregated across individuals (Physiological Synchrony -PS) can provide insight into group-level cognitive or emotional processes.Utilizing cheap and easy-to-use wearable sensors to measure physiology rather than their highend laboratory counterparts is desirable. Since it is currently ambiguous how different signal properties (arising from different types of measuring equipment) influence the detection of PS associated with mental processes, it is unclear whether, or to what extent, PS based on data from wearables compares to that from their laboratory equivalents. Existing literature has investigated PS using both types of equipment, but none compared them directly. In this study, we measure PS in electrodermal activity (EDA) and inter-beat interval (IBI, inverse of heart rate) of participants who listened to the same audio stream but were either instructed to attend to the presented narrative (n=13) or to the interspersed auditory events (n=13). Both laboratory and wearable sensors were used (ActiveTwo electrocardiogram (ECG) and EDA; Wahoo Tickr and EdaMove4). A participant's attentional condition was classified based on which attentional group they shared greater synchrony with. For both types of sensors, we found classification accuracies of 73% or higher in both EDA and IBI. We found no significant difference in classification accuracies between the laboratory and wearable sensors. These findings encourage the use of wearables for PS based research and for in-the-field measurements.

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Research on brain signals as indicators of a certain attentional state is moving from laboratory environments to everyday settings. Uncovering the attentional focus of individuals in such settings is challenging because there is usually limited information about real-world events, as well as a lack of data from the real-world context at hand that is correctly labeled with respect to individuals' attentional state. In most approaches, such data is needed to train attention monitoring models. We here investigate whether unsupervised clustering can be combined with physiological synchrony in the electroencephalogram (EEG), electrodermal activity (EDA), and heart rate to automatically identify groups of individuals sharing attentional focus without using knowledge of the sensory stimuli or attentional focus of any of the individuals. We used data from an experiment in which 26 participants listened to an audiobook interspersed with emotional sounds and beeps. Thirteen participants were instructed to focus on the narrative of the audiobook and 13 participants were instructed to focus on the interspersed emotional sounds and beeps. We used a broad range of commonly applied dimensionality reduction ordination techniques—further referred to as mappings—in combination with unsupervised clustering algorithms to identify the two groups of individuals sharing attentional focus based on physiological synchrony. Analyses were performed using the three modalities EEG, EDA, and heart rate separately, and using all possible combinations of these modalities. The best unimodal results were obtained when applying clustering algorithms on physiological synchrony data in EEG, yielding a maximum clustering accuracy of 85%. Even though the use of EDA or heart rate by itself did not lead to accuracies significantly higher than chance level, combining EEG with these measures in a multimodal approach generally resulted in higher classification accuracies than when using only EEG. Additionally, classification results of multimodal data were found to be more consistent across algorithms than unimodal data, making algorithm choice less important. Our finding that unsupervised classification into attentional groups is possible is important to support studies on attentional engagement in everyday settings.

Frontal alpha asymmetry refers to the difference between the right and left alpha activity over the frontal brain region. Increased activity in the left hemisphere has been linked to approach motivation and increased activity in the right hemisphere has been linked to avoidance or withdrawal. However, research on alpha asymmetry is diverse and has shown mixed results, which may partly be explained by the potency of the used stimuli to emotionally and motivationally engage participants. This review gives an overview of the types of affective stimuli utilized with the aim to identify which stimuli elicit a strong approach-avoidance effect in an affective context. We hope this contributes to better understanding of what is reflected by alpha asymmetry, and in what circumstances it may be an informative marker of emotional state. We systematically searched the literature for studies exploring event-related frontal alpha asymmetry in affective contexts. The search resulted in 61 papers, which were categorized in five stimulus categories that were expected to differ in their potency to engage participants: images & sounds, videos, real cues, games and other tasks. Studies were viewed with respect to the potency of the stimuli to evoke significant approach-avoidance effects on their own and in interaction with participant characteristics or condition. As expected, passively perceived stimuli that are multimodal or realistic, seem more potent to elicit alpha asymmetry than unimodal stimuli. Games, and other stimuli with a strong task-based component were expected to be relatively engaging but approach-avoidance effects did not seem to be much clearer than the studies using perception of videos and real cues. While multiple factors besides stimulus characteristics determine alpha asymmetry, and we did not identify a type of affective stimulus that induces alpha asymmetry highly consistently, our results indicate that strongly engaging, salient and/or personally relevant stimuli are important to induce an approach-avoidance effect.

Individuals that pay attention to narrative stimuli show synchronized heart rate (HR) and electrodermal activity (EDA) responses. The degree to which this physiological synchrony occurs is related to attentional engagement. Factors that can influence attention, such as instructions, salience of the narrative stimulus and characteristics of the individual, affect physiological synchrony. The demonstrability of synchrony depends on the amount of data used in the analysis. We investigated how demonstrability of physiological synchrony varies with varying group size and stimulus duration. Thirty participants watched six 10 min movie clips while their HR and EDA were monitored using wearable sensors (Movisens EdaMove 4 and Wahoo Tickr, respectively). We calculated inter-subject correlations as a measure of synchrony. Group size and stimulus duration were varied by using data from subsets of the participants and movie clips in the analysis. We found that for HR, higher synchrony correlated significantly with the number of answers correct for questions about the movie, confirming that physiological synchrony is associated with attention. For both HR and EDA, with increasing amounts of data used, the percentage of participants with significant synchrony increased. Importantly, we found that it did not matter how the amount of data was increased. Increasing the group size or increasing the stimulus duration led to the same results. Initial comparisons with results from other studies suggest that our results do not only apply to our specific set of stimuli and participants. All in all, the current work can act as a guideline for future research, indicating the amount of data minimally needed for robust analysis of synchrony based on inter-subject correlations.