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Nathaniel J. Zuk
Researcher at Trinity College, Dublin
Publications - 13
Citations - 123
Nathaniel J. Zuk is an academic researcher from Trinity College, Dublin. The author has contributed to research in topics: Neural coding & Perception. The author has an hindex of 4, co-authored 13 publications receiving 44 citations. Previous affiliations of Nathaniel J. Zuk include University of Rochester & Massachusetts Eye and Ear Infirmary.
Papers
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Journal ArticleDOI
Linear Modeling of Neurophysiological Responses to Speech and Other Continuous Stimuli: Methodological Considerations for Applied Research
Michael J. Crosse,Nathaniel J. Zuk,Nathaniel J. Zuk,Giovanni M. Di Liberto,Giovanni M. Di Liberto,Aaron R. Nidiffer,Sophie Molholm,Edmund C. Lalor,Edmund C. Lalor +8 more
TL;DR: In this paper, the authors focus on experimental design, data preprocessing and stimulus feature extraction, model design, training and evaluation, and interpretation of model weights, and demonstrate how to implement each stage in MATLAB using the mTRF toolbox.
Journal ArticleDOI
EEG-based classification of natural sounds reveals specialized responses to speech and music.
TL;DR: EEG techniques used here suggest that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds.
Journal ArticleDOI
Envelope reconstruction of speech and music highlights stronger tracking of speech at low frequencies.
Nathaniel J. Zuk,Jeremy W. Murphy,Richard B. Reilly,Edmund C. Lalor,Edmund C. Lalor,Edmund C. Lalor +5 more
TL;DR: In this article, a method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening was used to compare neural tracking of speech and music envelopes.
Journal ArticleDOI
Preferred Tempo and Low-Audio-Frequency Bias Emerge From Simulated Sub-cortical Processing of Sounds With a Musical Beat.
TL;DR: Using bottom-up processes alone is insufficient to produce beat-locked activity, and a learned and possibly top-down mechanism that scales the synchronization frequency to derive the beat frequency greatly improves the performance of tempo identification.
Posted ContentDOI
More than Words: Neurophysiological Correlates of Semantic Dissimilarity Depend on Comprehension of the Speech Narrative
TL;DR: Electrophysiological indices based on the semantic dissimilarity of words to their context reflect a listener’s understanding of those words relative to that context, and this work highlights the relative insensitivity of neural measures of low-level speech processing to speech comprehension.