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JOURNALS // Computer Research and Modeling // Archive

Computer Research and Modeling, 2023 Volume 15, Issue 3, Pages 675–690 (Mi crm1082)

ANALYSIS AND MODELING OF COMPLEX LIVING SYSTEMS

Optimization of the brain command dictionary based on the statistical proximity criterion in silent speech recognition task

A. Bernadotteabc, A. Mazurinb

a National University of Science and Technology MISIS, 4 Leninskiy pr., Moscow, 119049, Russia
b Faculty of Mechanics and Mathematics, Moscow State University, GSP-1, Leninskie Gory, Moscow, 119991, Russia
c LLC Neurosputnik, 96 pr. Vernadskogo, Moscow, 119571, Russia

Abstract: In our research, we focus on the problem of classification for silent speech recognition to develop a brain– computer interface (BCI) based on electroencephalographic (EEG) data, which will be capable of assisting people with mental and physical disabilities and expanding human capabilities in everyday life. Our previous research has shown that the silent pronouncing of some words results in almost identical distributions of electroencephalographic signal data. Such a phenomenon has a suppressive impact on the quality of neural network model behavior. This paper proposes a data processing technique that distinguishes between statistically remote and inseparable classes in the dataset. Applying the proposed approach helps us reach the goal of maximizing the semantic load of the dictionary used in BCI.
Furthermore, we propose the existence of a statistical predictive criterion for the accuracy of binary classification of the words in a dictionary. Such a criterion aims to estimate the lower and the upper bounds of classifiers’ behavior only by measuring quantitative statistical properties of the data (in particular, using the Kolmogorov – Smirnov method). We show that higher levels of classification accuracy can be achieved by means of applying the proposed predictive criterion, making it possible to form an optimized dictionary in terms of semantic load for the EEG-based BCIs. Furthermore, using such a dictionary as a training dataset for classification problems grants the statistical remoteness of the classes by taking into account the semantic and phonetic properties of the corresponding words and improves the classification behavior of silent speech recognition models.

Keywords: brain–computer interface, EEG, silent speech classification, graph dictionary selection algorithm, BCI, deep learning optimization, silent speech recognition, statistical proximity criterion.

UDC: 519.6, 519.7, 004.5

Received: 06.01.2023
Revised: 10.04.2023
Accepted: 10.05.2023

Language: English

DOI: 10.20537/2076-7633-2023-15-3-675-690



© Steklov Math. Inst. of RAS, 2024