EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

Year 2022, Volume: 5 Issue: 1, 124 - 137, 30.06.2022

Shams Qahtan Omar Omar Cengiz Tepe

https://doi.org/10.55117/bufbd.1099025

Cited By: 1

Abstract

Son on yılda, yapay zekâ (YZ) ve makine öğrenimi (MÖ) kullanımlarında bir artış görülmüştür. MÖ alanındaki son gelişmeler, farklı alanlar için elektroensefalografinin (EEG) kullanımına yeniden ilgi duyulmasına yol açmıştır. EEG, zihinsel iş yükünü ve yorgunluğu analiz etmek, beyin tümörlerini teşhis etmek ve merkezi sinir sistemi bozukluklarının rehabilitasyonu gibi tıbbi ve biyomedikal uygulamalarda; EEG tabanlı hareket analizi ve sınıflandırması ise, klinik uygulamalardan beyin-makine ara yüzüne ve robotik uygulamalara kadar birçok alanda yaygın olarak kullanılmaktadır. Bu makale, EEG sinyal işlemede kullanılan birçok MÖ algoritmalarının uygulamalarını gözden geçirmekte, yaygın olarak kullanılan algoritmaları, tipik uygulama senaryolarını, önemli ilerlemeleri ve mevcut sorunları tanıtmaktadır. Çalışmada, beyin-bilgisayar arayüzleri, bilişsel sinirbilim, beyin bozukluklarının teşhisi ve daha farklı konular dahil olmak üzere, EEG'deki mevcut MÖ uygulamaları araştırılmıştır. İlk olarak, evrişimli sinir ağı, destek vektör makineleri, K-en yakın komşu ve çok yönlü evrişim sinir ağı dahil olmak üzere EEG sinyal işlemede kullanılan MÖ algoritmalarının temel ilkeleri kısaca açıklanmıştır. Ayrıca EEG analizinde kullanılan MÖ uygulamalarına dair genel bir araştırma sunulmuştur. Sonuç olarak çalışmalarda en fazla DVM ve CNN yöntemlerinin kullanıldığı, çalışma başlıklarının ise ağırlıklı olarak epilepsi, BCI ve Duygu konularında en az ise Alkol, Uyku Durumları, Algı konularında yapıldığı belirlenmiştir.

Keywords

Elektroensefalografi, Makine öğrenme, Sinyal işleme, Özellik çıkarma, EEG

Supporting Institution

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Project Number

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Thanks

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A Review on Topics where Machine Learning has been used to Process EEG Signals

Abstract

The last decade has seen an increase in the use of artificial intelligence (AI) and machine learning (ML). Recent advances in the field of BC have led to renewed interest in the use of electroencephalography (EEG) for different fields. EEG is used in medical and biomedical applications such as analyzing mental workload and fatigue, diagnosing brain tumors and rehabilitation of central nervous system disorders; EEG-based motion analysis and classification is widely used in many areas from clinical applications to brain-machine interface and robotic applications. This article reviews the applications of many ML algorithms used in EEG signal processing, introduces commonly used algorithms, typical application scenarios, important advances and current problems. The study explored current applications of ML in EEG, including brain-computer interfaces, cognitive neuroscience, diagnosis of brain disorders, and more. First, the basic principles of ML algorithms used in EEG signal processing, including convolutional neural network, support vector machines, K-nearest neighbor and multidirectional convolutional neural network, are briefly explained. In addition, a general research on ML applications used in EEG analysis is presented. As a result, it was determined that the most SVM and CNN methods were used in the studies, and the study titles were mainly on epilepsy, BCI and Emotion, and the least on Alcohol, Sleeping States, Perception.

Keywords

Electroencephalography, Machine learning, Signal processing, Feature extraction, EEG

Project Number

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