Motor imagery EEG classification using algorithms and machine learning for ALS disease
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The cerebrum PC interface brings issues to light of basic ailments influencing the human sensory
system. The human sensory system is a mind boggling organization of neurons with a simple
flagging component. The cerebrum PC interface permits the framework to keep human mind
action to recognize sickness utilizing AI and PC vision. BCI information is alluded to as engine
symbolism. Engine symbolism is non-fixed information that is planned regarding recurrence and
time. In engine picture, recurrence is separated into various sign groups like alpha, beta, gamma,
and delta. Because of the great component of information and clamor relics, band division with
recoded signal is a basic undertaking. The information planning and vector development
processes require information decrease, however the information decrease process loses some
band esteem and can't correct the exact band of sign for the location of ailment and explicit
sickness problems. This review centers around highlight extraction from engine symbolism EEG
information and characterization of EEG information with human sickness.
Açıklama
Anahtar Kelimeler
Motor Imagery EEG, Artificial Neural Network (ANN), Brain-Computer Interface, Classification Of EEG
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Hameed Hameed, Mohammed Majeed. (2022). Motor imagery EEG classification using algorithms and machine leaning for ALS disease. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
