Vowel speech recognition from rat electroencephalography using long short-term memory neural network.

ABSTRACT: Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine le

SUBMITTER: Ham J 

PROVIDER: S-EPMC9223328 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature