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.

翻译：声化是一种数据可视化技术，通过心理声学参数（即用于传递信息的非语音音频信号）表达数据属性。本文研究基于脑电图嵌入对天文图像聚焦水平传递的心理声学参数所诱发的认知负荷进行二值估计。通过采用机器学习和深度学习方法，我们证明脑电图信号在以下方面具有可靠性：（a）认知负荷的二值估计；（b）区分简单与困难的视觉-听觉感知映射；以及（c）捕捉心理声学参数间的感知相似性。主要发现表明：（1）脑电图嵌入可可靠测量认知负荷，最高F1分数达0.98；（2）极端聚焦水平比中间水平更易通过听觉映射检测；（3）诱发相近认知负荷水平的心理声学参数倾向于生成相似的脑电图编码。