Motor imagery EEG classification using algorithms and machine learning for ALS disease
| dc.contributor.advisor | İnan, Timur | |
| dc.contributor.author | Hameed, Mohammed Majeed Hameed | |
| dc.date.accessioned | 2023-02-15T13:07:58Z | |
| dc.date.available | 2023-02-15T13:07:58Z | |
| dc.date.issued | 2022 | en_US |
| dc.date.submitted | 2022 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalı | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | 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. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/3337 | |
| dc.identifier.yoktezid | 768353 | |
| dc.institutionauthor | Hameed, Mohammed Majeed Hameed | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Motor Imagery EEG | en_US |
| dc.subject | Artificial Neural Network (ANN) | en_US |
| dc.subject | Brain-Computer Interface | en_US |
| dc.subject | Classification Of EEG | en_US |
| dc.title | Motor imagery EEG classification using algorithms and machine learning for ALS disease | |
| dc.type | Master Thesis |
