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 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.

Keywords: EEG; electrodermal activity; heart rate; inter-subject correlation; physiological synchrony; unsupervised clustering; unsupervised learning.

Figure 1

Overview of the processing pipeline, divided in signal pre-processing, mapping, clustering, and evaluation. EEG, electroencephalogram; EDA, electrodermal activity; ECG, electrocardiogram; HR, heart rate; PS, physiological synchrony; PCoA, principle coordinate analysis; mMDS, metric multidimensional scaling; nMDS, non-metric multidimensional scaling; UMAP, uniform manifold approximation and projection; MVMDS, multiview multidimensional scaling; MVSC, multiview spectral clustering.

Figure 2

Clustering accuracies utilizing physiological synchrony in EEG (red), EDA (yellow), heart rate (green) for different combinations of mappings (top-axis) and clustering methods (bottom-axis). Transparent top-to-bottom bars represent missing data. The dashed black line depicts significance level compared to chance level classification accuracies.

Figure 3

Clustering accuracies utilizing physiological synchrony in EEG and EDA (red), EEG and HR (yellow), EDA and heart rate (green), and EDA, EDA, and heart rate (purple) for different combinations of mappings (top-axis) and clustering algorithms (bottom-axis). The dashed black line depicts significance level compared to chance level classification accuracies.