Auditory Discrimination of Parametrically Sonified EEG Signals in Alzheimer’s Disease

<b>Background/Objectives</b>: Alzheimer's disease (AD) requires accessible and non-invasive biomarkers that can support early detection, especially in settings lacking specialized expertise. Sonification techniques may offer an alternative way to convey neurophysiological information through auditory perception. This study aimed to evaluate whether human listeners without EEG training can discriminate between sonified electroencephalographic (EEG) patterns from patients with AD and healthy controls. <b>Methods</b>: EEG recordings from 65 subjects (36 with Alzheimer's, 29 controls) from the Open-Neuro ds004504 dataset were used. Data were processed through sliding-window spectral analysis, extracting relative band powers across five frequency bands (delta: 1-4 Hz, theta: 4-8 Hz, alpha: 8-13 Hz, beta: 13-30 Hz, gamma: 30-45 Hz) and spectral entropy, aggregated across 10 topographic regions. Extracted features were sonified via parameter mapping to independent synthesis sources per frequency band, implemented in an interactive web interface (Tone.js v14.8.49) enabling auditory evaluation. Eight evaluators without EEG experience blindly classified subjects into two groups based solely on listening to the sonifications. <b>Results</b>: Listeners achieved a mean classification accuracy of 76.12% (SD = 17.95%; range: 49.25-97.01%), exceeding chance performance (<i>p</i> = 0.001, permutation test). Accuracy variability across evaluators suggests that certain auditory cues derived from the sonified features were consistently perceived. <b>Conclusions</b>: Parametric EEG sonification preserves discriminative neurophysiological information that can be perceived through auditory evaluation, enabling above-chance differentiation between Alzheimer's patients and healthy controls without technical expertise. This proof-of-concept study supports sonification as a complementary, accessible method for examining brain patterns in neurodegenerative diseases and highlight its potential contribution to the development of accessible diagnostic tools.