From pronounced to imagined: improving speech decoding with multi-condition EEG data

From pronounced to imagined: improving speech decoding with multi-condition EEG data

IntroductionImagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. Here, we investigated whether incorporating EEG data from overt (pronounced...

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Bibliographic Details
Main Authors: Denise Alonso-Vázquez, Omar Mendoza-Montoya, Ricardo Caraza, Hector R. Martinez, Javier M. Antelis
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Neuroinformatics
Subjects:
imagined speech classification
EEG-based classification
overt speech
EEGNET
brain-computer interfaces
Online Access:https://www.frontiersin.org/articles/10.3389/fninf.2025.1583428/full
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