Balım, Mustafa AlperAcır, Nurettin2021-03-202021-03-2020209781728172064http://doi.org/10.1109/SIU49456.2020.9302470https://hdl.handle.net/20.500.12885/128328th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413Motor imagery signals are one of the electroencephalography signals that occurs by subjects imagining the movement of a body limb and they used in brain computer interface applications. With the use of deep neural networks in motor imagery signal classification, classification performance has increased. Deep belief networks are one of the networks that increase performance. In this study, using a stacked autoencoders and BCI IV 2b dataset, a deep belief network and education method with more robust and higher performance is proposed. Average classification performance is obtained as 80.44%. © 2020 IEEE.trinfo:eu-repo/semantics/closedAccessbrain computer interfacedeep belief networksmotor imagery signal calssificationstacked autoencodersConference Object10.1109/SIU49456.2020.93024702-s2.0-85100293998N/AN/A