Article

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals

2024-03-14

Abstract excerpt

<h4>Objective</h4> This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture shoul...

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Literature Corpus work: ee6b2faf-0970-5e3a-acd0-553230c4658b
DOI: 10.1101/2024.03.11.584533

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