Imaginary and Real Speech-Related EEG Patterns in the Neural Network Approach

It is quite clear that a person can make mistakes in the process of speech: badly pronounced letters, “missing” syllables. This situation poses certain difficulties for developments in the field of mental or inner speech-based BCI technology. The inability to separate errors from correct speech can...

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Bibliographic Details
Published in:Human physiology 2022-12, Vol.48 (6), p.644-655
Main Authors: Kiroy, V., Bakhtin, O., Krivko, E., Lazurenko, D., Aslanyan, E., Shaposhnikov, D.
Format: Article
Language:English
Subjects:
Biomedical and Life Sciences
Biomedicine
Classification
EEG
Hemispheric laterality
Human Physiology
Life Sciences
Neural networks
Speech
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Summary:It is quite clear that a person can make mistakes in the process of speech: badly pronounced letters, “missing” syllables. This situation poses certain difficulties for developments in the field of mental or inner speech-based BCI technology. The inability to separate errors from correct speech can significantly reduce the effectiveness of BCIs. In this study, we used words representing the direction in space and pseudowords made randomly from individual syllables, that is, pseudowords were phonetically close to words. The aim of the study was to explore the possibilities of neural network classification of words and pseudo-words-related EEG coherence patterns in the task of mental and spoken speech. It was shown that the preparation and pronunciation of pseudowords takes a long time and is performed non-lexically with sublexical components. It is shown that the structure of EEG patterns with high coherence values registered in both hemispheres in the gamma range is homotopically equivalent and seems to be related to attention mechanisms. The EEG patterns with low coherence are significantly different, which indicates the specifics of right hemisphere involvement in speech processes. The most stable coherence patterns with a significant difference between words and pseudo-words are found in the left hemisphere in gamma frequencies. The main conclusion is that the neural network classification of inner speech-related EEG coherence patterns based on the multilayer perceptron (MLP) demonstrates an accuracy up to 90% in the recognition of words and pseudowords.
ISSN:0362-1197
1608-3164
DOI:10.1134/S0362119722320019