A basic identification of late auditory evoked potentials at infrasound frequencies: Support via neural network–based signal processing. Issue 2 (June 2023)

• Record Type: Journal Article
• Authors: Koch, Christian
• Is Part Of: Journal of low frequency noise, vibration, and active control. Volume 42:Issue 2(2023)
• Page Start: 719
• Page End: 728
• Publication Date: 2023-06
• Subjects: Infrasound -- machine learning -- deep learning -- auditory evoked potentials -- autoencoder; Vibration -- Periodicals; Noise -- Periodicals; Sound -- Periodicals; Damping (Mechanics) -- Periodicals; Damping (Mechanics); Noise; Sound; Vibration; Periodicals; 620.205
• DOI: 10.1177/14613484221138812
• Languages: English
• ISSNs: 1461-3484

Abstract

Concerns about potential health risks from infrasound exposure require objective investigation methods for the perception of infrasound by humans. Neural-network methods were applied to identify weak infrasound evoked potentials and to investigate the efficacy of these techniques. Two simple networks – a fully connected three-layer feed-forward network and a two-layer autoencoder – were designed for the analysis of electroencephalography (EEG) data recorded in response to infrasound stimuli presented. Late auditory evoked potentials (LAEPs) were detected and correlation factors between the target and the output of the networks were determined to assess the differentiation ability of the two networks. Both networks were able to differentiate infrasound evoked potentials from noise but showed different performance levels with respect to a differentiation threshold. The results can be applied to objectively identify perception events by infrasound in humans.