Estimating Attention Allocation by Electrodermal Activity

Electrodermal activity (EDA) represents changes in the electrical activity of the palmar skin and serves as an indicator of sympathetic nervous system activity. This paper presents a novel method for estimating attention allocation under divided attention conditions using only EDA data. Our approach involves the use of the low-frequency power spectrum derived from the phasic component of EDA associated with attentional focus, combined with a machine learning classification model. We conducted three user studies aimed at estimating participants' attention allocation during the performance of simple tasks under both visual and auditory stimuli where the frequencies of the stimuli were different, identical, or ambiguous. The goal was to estimate whether participants focused on visual or auditory stimuli. The results showed that our method could estimate attention allocation with the accuracy of 96% and 73% when the frequencies of the two stimuli were different and ambiguous, respectively, and could not estimate when the frequencies were identical.