Affiliation:
1. National Research Tomsk Polytechnic University
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.
Funder
Russian Foundation for Basic Research
Publisher
MONOMAX Limited Liability Company
Reference24 articles.
1. V.V. Grubov, A.E. Runnova, M.K. Kurovskaуa, A.N. Pavlov, A.A. Koronovskii, A.E. Hramov, “Demonstration of brain noise on human EEG signals in perception of bistable images”, Proc. SPIE. 2016. V. 9707. DOI: 10.1117/12.2207390
2. P. Sotnikov, K. Finagin, S. Vidunova, “Selection of optimal frequency bands of the electroencephalogram signal in eye-brain-computer interface”, Procedia Computer Science. 2017, vol. 103, pp. 168-175.
3. A.N. Vasilyev, S.P. Liburkina, A.Y. Kaplan, “Lateralization of EEG patterns in humans during motor imagery of arm movements in the brain-computer interface”, Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I.P. Pavlova, 2016, vol. 66, № 3, pp. 302-312.
4. V.A. Maksimenko, S. Heukelum, V.V. Makarov, J. Kelderhuis, A. Lüttjohann, A.A. Koronovskii, A.E. Hramov, G. Luijtelaar, “Absence seizure control by a brain computer interface”, Scientific Reports. 2017, vol. 7, p. 2487.
5. W. Hsu, I. Chiang, “Application of neural network to brain-computer interface”, 2012 IEEE International Conference on Granular Computing, Hangzhou, 2012, pp. 163-168. doi: 10.1109/GrC.2012.6468559