EEG-based Cognitive Load Estimation of Acoustic Parameters for Data Sonification

TL;DR

This study demonstrates that EEG signals can reliably estimate cognitive load induced by psychoacoustic parameters in data sonification, enabling better understanding of perceptual mappings and similarities.

Contribution

It introduces a machine learning approach to binary classify cognitive load from EEG in the context of acoustic data visualization, highlighting the reliability of EEG embeddings.

Findings

EEG embeddings achieve a peak F1-score of 0.98 for cognitive load detection.

Extreme focus levels are more easily detected than intermediate levels.

Similar psychoacoustic parameters induce comparable EEG patterns.

Abstract

Sonification is a data visualization technique which expresses data attributes via psychoacoustic parameters, which are non-speech audio signals used to convey information. This paper investigates the binary estimation of cognitive load induced by psychoacoustic parameters conveying the focus level of an astronomical image via Electroencephalogram (EEG) embeddings. Employing machine learning and deep learning methodologies, we demonstrate that EEG signals are reliable for (a) binary estimation of cognitive load, (b) isolating easy vs difficult visual-to-auditory perceptual mappings, and (c) capturing perceptual similarities among psychoacoustic parameters. Our key findings reveal that (1) EEG embeddings can reliably measure cognitive load, achieving a peak F1-score of 0.98; (2) Extreme focus levels are easier to detect via auditory mappings than intermediate ones, and (3) psychoacoustic parameters inducing comparable cognitive load levels tend to generate similar EEG encodings.