Application of the "Stripe" Algorithm for Online Decoding of the EEG Patterns

Abstract

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.