  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.
