An Overview of Classification of Electrooculography (EOG) Signals by Machine Learning Methods

Author:

SUİÇMEZ Alihan1,TEPE Cengiz2,ODABAS Mehmet Serhat3

Affiliation:

1. KASTAMONU ÜNİVERSİTESİ, MÜHENDİSLİK VE MİMARLIK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ

2. ONDOKUZ MAYIS ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ

3. ONDOKUZ MAYIS ÜNİVERSİTESİ, BAFRA MESLEK YÜKSEKOKULU, BİLGİSAYAR TEKNOLOJİLERİ BÖLÜMÜ

Abstract

The distribution of the studies conducted between 2011-2021 in the fields of (Electrooculography) EOG and eye movements, EOG and wheelchair, EOG and eye angle, EOG and sleep state, EOG and mood estimation and EOG and game application was determined according to years, and the most cited studies were examined and presented. The study areas are listed as Eye Movement Classification, Wheelchair, Sleep state, Eye Angle, Mood State and Game Applications from the most to the least number of articles. When we examine in terms of the number of citations, they are listed as Sleeping state, Eye Movement Classification, Wheelchair, Eye Angle, Mood State and Game Applications, from the most to the least. In these studies, it has been tried to make the lives of people who have become disabled in various ways better by using the brain-computer interface with machine learning.

Publisher

Gazi Universitesi Fen Bilimleri Dergisi Part C: Tasarim ve Teknoloji

Subject

General Medicine

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