Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.

ABSTRACT: Objective.Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field.Approach.In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task.Main results.We show that (1)

SUBMITTER: Berezutskaya J 

PROVIDER: S-EPMC10510111 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature