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Paper Detail

Paper: PS-2A.8
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Linear-nonlinear Bernoulli modeling for quantifying temporal coding of phonemes in brain responses to continuous speech
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Nathaniel Zuk, Trinity College Dublin, Ireland; Giovanni Di Liberto, École Normale Supérierue, France; Edmund Lalor, University of Rochester, United States
Abstract: The electroencephalographic (EEG) response to a sound of interest is often quantified by averaging time-locked signals over many repetitions in order to get an event-related potential (ERP). While this technique can identify an average response, it does not easily allow one to validate the robustness of that response nor variation of the response over repetitions of the sound. Here, we extend the ERP technique as a linear-nonlinear Bernoulli (LNB) model, inspired by neural models, in order to develop a framework for decoding the timing of stimulus events. We use this technique to analyze EEG recordings during presentations of continuous speech and examine neural responses to phonemes, which have been shown to have characteristic EEG responses. Pattern analysis of the confusion between phonemes separates phonemes into vowel and constants, indicating separate ERPs that can robustly predict these phoneme classes. We also find that vowels are decoded more accurately than consonants, and the time course of vowel predictability tracks the rhythm of vowels, while consonant predictability does not track the rhythm of consonants. Overall, we demonstrate a specific instance in which a linear-nonlinear Bernoulli modeling framework can be used to compare ERPs and quantify the ability to decode stimulus events from EEG.