Predicting early auditory evoked potentials using a computational model of auditory-nerve processing

Miguel Temboury-Gutierrez*, Gerard Encina-Llamas, Torsten Dau

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Non-invasive electrophysiological measures, such as auditory evoked potentials (AEPs), play a crucial role in diagnosing auditory pathology. However, the relationship between AEP morphology and cochlear degeneration remains complex and not well understood. Dau [J. Acoust. Soc. Am. 113, 936-950 (2003)] proposed a computational framework for modeling AEPs that utilized a nonlinear auditory-nerve (AN) model followed by a linear unitary response function. While the model captured some important features of the measured AEPs, it also exhibited several discrepancies in response patterns compared to the actual measurements. In this study, an enhanced AEP modeling framework is presented, incorporating an improved AN model, and the conclusions from the original study were reevaluated. Simulation results with transient and sustained stimuli demonstrated accurate auditory brainstem responses (ABRs) and frequency-following responses (FFRs) as a function of stimulation level, although wave-V latencies remained too short, similar to the original study. When compared to physiological responses in animals, the revised model framework showed a more accurate balance between the contributions of auditory-nerve fibers (ANFs) at on- and off-frequency regions to the predicted FFRs. These findings emphasize the importance of cochlear processing in brainstem potentials. This framework may provide a valuable tool for assessing human AN models and simulating AEPs for various subtypes of peripheral pathologies, offering opportunities for research and clinical applications.

Original languageEnglish
JournalJournal of the Acoustical Society of America
Issue number3
Pages (from-to)1799-1812
Publication statusPublished - 2024


Dive into the research topics of 'Predicting early auditory evoked potentials using a computational model of auditory-nerve processing'. Together they form a unique fingerprint.

Cite this