Classifier of Motor EEG Images for Real Time BCI

IF 1 Q4 OPTICS
L. A. Stankevich, S. A. Kolesov
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引用次数: 0

Abstract

The work is devoted to the development of a classifier of motor activity patterns based on electroencephalograms (EEG) for a real-time brain-computer interface (BCI), which can be used in contactless control systems. Conducted studies of various methods for classifying motor EEG images have shown that their effectiveness significantly depends on the implementation of the stages of information processing in the BCI. The most effective classification method turned out to be the support vector machine. However, its long operating time and lack of accuracy make it difficult to use for implementing real-time BCI. Therefore, a classifier was developed using an ensemble of detectors, each of which is trained to recognize its own motor EEG image. A new EEG analysis algorithm based on event functions was applied. A study of the classifier showed that it is possible to achieve detection accuracy of 98.5% with an interface delay of 230 ms.

Abstract Image

用于实时脑机接口的运动脑电信号分类器
该工作致力于开发基于脑电图(EEG)的运动活动模式分类器,用于实时脑机接口(BCI),可用于非接触式控制系统。对各种运动脑电图像分类方法的研究表明,它们的有效性在很大程度上取决于脑机接口中信息处理阶段的实施。最有效的分类方法是支持向量机。然而,它的工作时间长,精度低,难以用于实现实时BCI。因此,使用检测器集合开发了分类器,每个检测器都经过训练以识别自己的运动脑电图像。提出了一种新的基于事件函数的脑电分析算法。对该分类器的研究表明,在接口延迟为230 ms的情况下,可以实现98.5%的检测准确率。
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来源期刊
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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