Abstract
An electroencephalographic signal is characterized by high complexity and character, which changes dynamically over time. At the same time, it should be noted that EEG signals in the field of Motor Imagery are increasingly more often used by scientists, among others, to help people with disabilities. Decoding these signals is important from the perspective of modern solutions based on the brain-computer interface technology. It is possible to isolate both spatial, as well as temporal features from an EEG signal using a ConvNet. This research paper suggests combining numerous ConvNet – Convolutional Neural Network models in terms of the EEG signal. The suggested multilayer ConvNets indicate high performance coefficients on a test data set. The method advocated in this article achieves a degree of accuracy on a data set at a precision level of 74.5%.
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Paszkiel, S., Dobrakowski, P. (2021). The Use of Multilayer ConvNets for the Purposes of Motor Imagery Classification. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2021: Recent Achievements in Automation, Robotics and Measurement Techniques. AUTOMATION 2021. Advances in Intelligent Systems and Computing, vol 1390. Springer, Cham. https://doi.org/10.1007/978-3-030-74893-7_2
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