Cognitive processing of a common stimulus synchronizes brains, hearts, and eyes

Madsen, Jan; Parra, Lucas C.

doi:10.1101/2021.09.16.460722

Cited by 1 publication

(2 citation statements)

References 99 publications

(211 reference statements)

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“…Applying the clustering algorithms to physiological synchrony in EDA or heart rate resulted in lower classification accuracies than in EEG, and generally led to performance near theoretical chance level. This is in line with other work, where synchronous changes in peripheral modalities have been shown to reflect attentional engagement with narrative stimuli less robustly than EEG (Ki et al, 2016;Perez et al, 2020;Stuldreher et al, 2020a;Madsen and Parra, 2021).…”

Section: Clustering Performance Using Physiological Synchrony In Either Eeg Eda or Heart Ratesupporting

confidence: 92%

“…It may be so that combining multiple modalities compensates for potential noisy observations in any of the modalities. Recent work of Madsen and Parra ( 2021 ) showed that physiological synchrony in EEG and heart rate in response to instructional videos are co-modulated. Thus, one noisy measurement may be compensated for by another measurement.…”

Section: Discussionmentioning

confidence: 99%

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Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony

Stuldreher¹,

Merasli²,

Thammasan³

et al. 2022

Front. Neuroergonomics

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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.

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Section: Clustering Performance Using Physiological Synchrony In Either Eeg Eda or Heart Ratesupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%