Biology subjects

Thielen, J.

Publications and source records attributed to Thielen, J..

2 recordsLinked to original sources

bioRxiv2024

Fast and robust objective EEG audiometry

The current gold standard of audiometry relies on subjective behavioral responses, which is impractical and unreliable for certain groups such as children, individuals with severe disabilities, or the disabled elderly. This study presents a novel electroencephalography (EEG) system that is easy to setup and estimates audiometric thresholds quickly. Air-conduction audiometric thresholds at 250, 500, 1000, 2000, 4000, and 8000 Hz and 5 dB resolution were estimated from ten elderly patients with asymmetric sensorineural hearing loss and five normal hearing young adults using three different systems: the novel EEG system, conventional pure-tone audiometry (PTA), and an automated behavioral test with the same stimulus properties as in the EEG test. EEG data was collected for 15 minutes from 32 semi-dry EEG electrodes. Later, the EEG system was trimmed to 8 electrodes and 7.5 minutes of data with satisfactory results. Correlation and regression analysis validated the hearing thresholds derived from both EEG configurations relative to the behavioral hearing thresholds-Pearsons correlation of 0.82 between PTA and 8-electrode 7.5-minute EEG data. The results of this study open the door to fast and objective hearing threshold estimation with EEG.

neuroscience↗

bioRxiv2019

DeepRF: Ultrafast population receptive field mapping with deep learning

Population receptive field (pRF) mapping is an important asset for cognitive neuroscience. The pRF model is used for estimating retinotopy, defining functional localizers and to study a vast amount of cognitive tasks. In a classic pRF, the cartesian location and receptive field size are modeled as a 2D Gaussian kernel in visual space and are estimated by optimizing the fit between observed responses and predicted responses. In the standard framework this is achieved using an iterative gradient descent algorithm. This optimization is time consuming because the number of pRFs to fit (e.g., fMRI voxels) is typically large. This computation time increases further with the complexity of the pRF model (e.g., adding HRF parameters, surround suppression and uncertainty measures). Here, we introduce DeepRF, which uses deep convolutional neural networks to estimate pRFs. We compare the performance of DeepRF with that of the conventional method using a synthetic dataset for which the ground truth is known and an empirical dataset. We show that DeepRF achieves state-of-the-art performance while being more than 3 orders of magnitude faster than the conventional method. This enables easier and faster modeling of more complex pRF models, resolving an important limitation of the conventional approach.

neuroscience↗