Corentin Puffay

This study uses large language models to analyze how attention affects neural tracking of linguistic context in multi-talker speech environments.

This study uses EEG and deep learning to classify native versus foreign speech perception based on linguistic features and observes differences in neural tracking patterns.

The paper uses a convolutional neural network to analyze EEG data and shows it better captures linguistic speech features than linear models.