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

Q4 Computer Science
Alexandra Bernadotte, Alexandr D. Mazurin
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引用次数: 0

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.
基于统计接近准则的无声语音识别任务脑命令字典优化
本研究主要针对无声语音识别的分类问题,开发一种基于脑电图(EEG)数据的脑机接口(BCI),为身心残障人士提供辅助,拓展人类日常生活能力。我们之前的研究表明,一些单词的不发音会导致脑电图信号数据几乎相同的分布。这种现象对神经网络模型的行为质量有抑制作用。本文提出了一种数据处理技术,用于区分数据集中统计上的远程类和不可分割类。应用所提出的方法可以帮助我们达到最大化脑机接口中使用的字典的语义负载的目标。此外,我们提出了字典中词的二分类准确性的统计预测准则的存在性。该准则旨在仅通过测量数据的定量统计特性(特别是使用Kolmogorov - Smirnov方法)来估计分类器行为的下界和上界。我们表明,通过应用提出的预测标准可以实现更高水平的分类精度,从而可以根据基于脑电图的脑机接口的语义负载形成优化的字典。此外,使用这样的字典作为分类问题的训练数据集,通过考虑相应单词的语义和语音属性,赋予类的统计距离性,并改善无声语音识别模型的分类行为。
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来源期刊
Computer Research and Modeling
Computer Research and Modeling Computer Science-Computational Theory and Mathematics
CiteScore
0.80
自引率
0.00%
发文量
82
审稿时长
15 weeks
期刊介绍: The journal publishes original research papers and review articles in the field of computer research and mathematical modeling in physics, engineering, biology, ecology, economics, psychology etc. The journal covers research on computer methods and simulation of systems of various nature in the leading scientific schools of Russia and other countries. Of particular interest are papers devoted to simulation in thriving fields of science such as nanotechnology, bioinformatics, and econophysics. The main goal of the journal is to cover the development of computer and mathematical methods for the study of processes in complex structured and developing systems. The primary criterion for publication of papers in the journal is their scientific level. The journal does not charge a publication fee. The decision made on publication is based on the results of an independent review. The journal is oriented towards a wide readership – specialists in mathematical modeling in various areas of science and engineering. The scope of the journal includes: — mathematical modeling and numerical simulation; — numerical methods and the basics of their application; — models in physics and technology; — analysis and modeling of complex living systems; — models of economic and social systems. New sections and headings may be included in the next volumes.
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