Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.

ABSTRACT:

Objective

Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice.

Approach

In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We the

SUBMITTER: Angrick M 

PROVIDER: S-EPMC11451764 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature