EEG-based Evaluation of Cognitive Workload Induced by Acoustic Parameters for Data Sonification

TL;DR

This study introduces an EEG-based method to automatically evaluate cognitive workload induced by different acoustic parameters in data sonification, revealing correlations between EEG signals, user accuracy, and perceived workload.

Contribution

It is the first to automatically estimate cognitive load in data sonification using EEG, combining explicit, implicit, and performance measures for comprehensive assessment.

Findings

Low cognitive load acoustic parameters lead to higher mapping accuracy.

EEG alpha band power is higher for low cognitive load parameters.

EEG features can classify cognitive load with a peak F1-score of 0.64.

Abstract

Data Visualization has been receiving growing attention recently, with ubiquitous smart devices designed to render information in a variety of ways. However, while evaluations of visual tools for their interpretability and intuitiveness have been commonplace, not much research has been devoted to other forms of data rendering, eg, sonification. This work is the first to automatically estimate the cognitive load induced by different acoustic parameters considered for sonification in prior studies. We examine cognitive load via (a) perceptual data-sound mapping accuracies of users for the different acoustic parameters, (b) cognitive workload impressions explicitly reported by users, and (c) their implicit EEG responses compiled during the mapping task. Our main findings are that (i) low cognitive load-inducing (ie, more intuitive) acoustic parameters correspond to higher mapping accuracies, (ii) EEG spectral power analysis reveals higher $\alpha$ band power for low cognitive load parameters, implying a congruent relationship between explicit and implicit user responses, and (iii) Cognitive load classification with EEG features achieves a peak F1-score of 0.64, confirming that reliable workload estimation is achievable with user EEG data compiled using wearable sensors.