Abstract
The article is devoted to the problem of recognition of motor imagery based on electroencephalogram (EEG) signals, which is associated with many difficulties, such as the physical and mental state of a person, measurement accuracy, etc. Artificial neural networks are a good tool in solving this class of problems. Electroencephalograms are time signals, Gramian Angular Fields (GAF), Markov Transition Field (MTF) and Hilbert space-filling curves transformations are used to represent time series as images. The paper shows the possibility of using GAF, MTF and Hilbert space-filling curves EEG signal transforms for recognizing motor patterns, which is further applicable, for example, in building a brain-computer interface.
Highlights
Electroencephalography is one of the most popular non-invasive methods for studying brain activity today
Convolutional Neural Network (CNN) is a special architecture of artificial neural networks aimed at effective pattern recognition
The resulting matrix is converted into an image, which is fed to the input of the convolutional neural network
Summary
Electroencephalography is one of the most popular non-invasive methods for studying brain activity today. The study of EEG is associated with many difficulties, such as the dependence of signals on age, time of day, the presence of noise, interference, and weak structuring. The use of artificial neural network (ANN) in applied areas, such as the brain-computer interface, is a perspective direction today [2,3,4,5,6]. A key factor in the success of human activities recognition using EEG is the effective use of data obtained from measurement sensors. The method proposed in [11] is used In this method, the time series is converted into images, after which the convolutional neural network is used to analyze them. The processes associated with motor imagery are extremely complex, changes in the time-frequency structure of electroencephalograms are not systematic and vary for each person. The change in the EEG signal can be used to recognize motor patterns
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