Annotation

Annotation: The problem of finding approaches to creating data sets based on features extracted from time series recorded when working with the 14-channel neural interface EMOTIV EPOC+ in the process of formation of motor imageries by independent subjects for controlling the computer mouse cursor is considered. The purpose of the work is to study a number of indicators, the values of which are calculated by performing certain mathematical operations on various fragments of 14 time series, as tools for generating feature values. The study should help to identify those indicators, the use of which to form features in datasets ensures the development of motor imagery classifiers that differ in the highest possible quality of data classification. Previously, the time series were filtered using a 5th order Butterworth filter, which made it possible to solve the problem of removing noise artifacts from the time series. To assess the influence of the length of a fragment of a time series used to form the value of a feature based on a particular indicator on the final quality of data classification, time intervals (time frames) of 1, 2 and 3 seconds were considered. During the research, the development of SVM (Support Vector Machine), RF (Random Forest) and MLP (Multi Layer Perceptrone) classifiers of motor imageries was carried out. The experimental results showed the feasibility of working with indicators calculated based on Shannon entropy and Higuchi fractal dimension on a time frame of 3 seconds. In this case, it is possible to ensure high quality of classification of motor imageries, assessed using the F-measure. In particular, SVM, RF and MLP classifiers developed on the basis of datasets in which features are calculated using Shannon entropy have the maximum Fmeasure values. These F-measure values are 0.82, 0.88 and 0.73, respectively.