Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony.

ABSTRACT: 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 att

SUBMITTER: Stuldreher IV 

PROVIDER: S-EPMC10790845 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature