“条纹”算法在脑电模式在线解码中的应用

Q4 Engineering
M. Lipkovich, A. R. Sagatdinov
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引用次数: 0

摘要

在本文中,我们考虑了根据脑电图信号来确定被试打算用哪只手做一个动作的问题。这项任务的相关性是由于脑机接口的广泛普及,其中脑电图是获取大脑信号的主要非侵入性方法之一。为了解决这个问题,从运动之前的信号片段中选择时间和频率特征,并将其馈送到分类机器学习模型的输入。与标准的监督学习设置不同,它假设没有预定义的训练数据集,并且模型的训练样本是一个接一个接收的。因此,模拟一种情况,在这种情况下,模型必须与一个新的主题一起工作,并实时调整。传统的线性模型训练方法是随机梯度下降法。在此之前,已经证明了Yakubovich提出的针对某一问题的“条纹”算法比随机梯度下降算法收敛速度更快。然而,这是通过对样本的每个特征执行算法步骤来实现的。因此,该版本的“Stripe”不适合处理高维数据。本文讨论的是“Stripe”的另一个版本,它没有这个缺点。在BCI竞争II数据集上,与基于随机梯度下降的传统线性模型相比,该算法具有更高的单步学习速率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of the "Stripe" Algorithm for Online Decoding of the EEG Patterns
In this paper, we consider the problem of determining the hand with which the subject intends to make a movement according to the signals of the electroencephalogram. The relevance of the task is due to the wide spread of brain-computer interfaces, where electroencephalography is one of the main non-invasive methods for obtaining signals from the brain. To solve the problem, temporal and frequency features are selected from the segments of signals preceding the movement, which are fed to the input of the classification machine learning model. In contrast to the standard supervised learning setup, it is assumed that there is no predefined training data set and the training samples for the model are received one after another. Thus, a situation is simulated in which the model must work with a new subject and adjust to them in real time. The traditional method for training linear models in such a paradigm is stochastic gradient descent. Previously, it was shown that the "Stripe" algorithm developed by Yakubovich for a certain problem has a higher convergence rate than stochastic gradient descent. However, this is achieved by performing algorithm step on each feature of the sample. Thus, that version of "Stripe" is not suitable for working with high-dimensional data. This article discusses another version of "Stripe" that does not have this drawback. It is shown that the proposed algorithm has a higher rate of one learning step compared to traditional linear models based on stochastic gradient descent on the BCI competition II dataset.
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来源期刊
Mekhatronika, Avtomatizatsiya, Upravlenie
Mekhatronika, Avtomatizatsiya, Upravlenie Engineering-Electrical and Electronic Engineering
CiteScore
0.90
自引率
0.00%
发文量
68
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