Classification of Motor Imaginary Signals Using Stacked Autoencoders

Küçük Resim Yok

Tarih

2020

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Institute of Electrical and Electronics Engineers Inc.

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Motor 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.

Açıklama

28th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- -- 166413

Anahtar Kelimeler

brain computer interface, deep belief networks, motor imagery signal calssification, stacked autoencoders

Kaynak

2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

WoS Q Değeri

N/A

Scopus Q Değeri

N/A

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