C. Jehn

CORSICA suppresses cochlear implant artifacts in speech-evoked EEG data using cross-correlation with speech stimuli, outperforming alternative methods with minimal manual intervention.

This study uses MEG and machine learning to decode auditory and visual mental imagery, showing deep learning models can capture cross-modal neural representations even from non-task-relevant regions.

This study compares cortical speech tracking and attentional modulation between hearing aid users, cochlear implant users, and typical hearing individuals in noisy environments.

This study investigates how the brain processes audiovisual speech from natural videos, avatars, and degraded videos, revealing distinct neural tracking patterns in different frequency bands.