Imagined speech can be decoded from low- and cross-frequency intracranial EEG features.

ABSTRACT: Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, pho

SUBMITTER: Proix T 

PROVIDER: S-EPMC8748882 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature