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Neural network analysis of electroencephalograms based on their graphical representation
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) and Markov Transition Field (MTF) transformations are used to represent time series as images. The paper shows the possibility of using GAF and MTF EEG signal transforms for recognizing motor patterns, which is further applicable, for example, in building a brain-computer interface- Conference Paper
- info:eu-repo/semantics/publishedVersion
- info:eu-repo/semantics/conferencePaper
- motor imagery recognition
- electroencephalogram
- Gramian Angular Field
- Markov Transition Field
- Convolutional Neural Network
- ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ
- ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ
- ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½ΡΠ΅ΡΠ°Π»ΠΎΠ³ΡΠ°ΠΌΠΌΡ
- ΠΌΠ°ΡΠΊΠΎΠ²ΡΠΊΠΈΠ΅ ΠΏΠΎΠ»Ρ
- ΡΠ²Π΅ΡΡΠΎΡΠ½ΡΠ΅ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ
- ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΈΠ³Π½Π°Π»ΠΎΠ²