Determining the Demands of Disabled People by Artificial Intelligence Methods

Abstract

Analysis of brain activities and remote control are among the current issues that are being studied. Analysis of signals arising during brain functions is electroencephalography (EEG). EEG signals have intellectual, visual stimulation, and motion resultant forms. Especially, EEG signals generated by visual stimulus are within the scope of this study. In this study, research was carried out on the classification of EEG signals formed in a person looking at visual figures. For these studies, first of all, EEG signals from the brain were recorded with images and filtered to remove noise. Then, the features were extracted from the signals. In this study, Moment 5 feature was also used in addition to the features used in many studies such as mean, median, standard deviation and entropy. Then, classification was made using Support Vector Machine (SVM), k Nearest Neighbor (KNN), and Decision Tree (DT) algorithms. Classification was made for 4 different visual shapes used, since these shapes are square, circle, triangle, and star, and the same categorical names were used in the classification stage. As a result of the classification of EEG signals; SVM and KNN algorithms have determined which shape is viewed with 99.99% accuracy. These results show that different signals are produced in the brain according to the structure of the shape viewed. This situation shows that it can be used as a method to give patients the opportunity to express their requests just by looking or thinking.

Keywords

• EEG
• brain signal
• visual stimulus
• shape prediction

Engellilerin Taleplerinin Yapay Zeka Yöntemleriyle Belirlenmesi

Abstract

Beyin aktiviteleri ve uzaktan kontrol üzerinde çalışma yapılan güncel konular arasında önemli yer tutmaktadırlar. Beyin işlevleri sırasında ortaya çıkan sinyallerin analizi elektroansefalografi (EEG) olmaktadır. Sinyallerin düşünsel, görsel ve hareket sonucu oluşmak gibi şekilleri mevcuttur. Özellikle görsel uyaran ile oluşan EEG sinyalleri bu çalışmanın alanına girmektedir. Bu çalışmada görsel şekillere bakan bir kişide oluşan EEG sinyallerinin sınıflandırılması üzerine araştırmalar yapılmaktadır. Bu amaçla EEG sinyalleri kaydedilmiş ve filtrelenerek gürültüden arındırılmıştır. Daha sonra sinyallerden öznitelikler çıkarılmıştır. Bu çalışmada özellikle ortalama, medyan, standart sapma ve Entropi gibi birçok çalışmada kullanılan özniteliklerin yanı sıra Moment 5 özniteliği de kullanılmıştır. Daha sonra Destek vektör makinesi (SVM), k en yakın komşu(KNN), ve karar ağacı(DT) algoritmaları kullanarak sınıflandırma yapılmıştır. Sınıflandırma yapılırken 4 farklı sınıf kullanılmıştır. Bu sınıflara kare, daire, üçgen ve yıldız isimleri verilmiştir. Sonuç olarak SVM ve KNN algoritmaları 99.99% doğruluk oranı ile bakılan şeklin hangisi olduğunu elde etmişlerdir. Böylece görülüyor ki beyin bakılan şeklin yapısına göre farklı sinyaller üretmektedir. Bu durum hastaların sadece bakarak veya düşünerek isteklerini bildirme imkanı tanımak için bir yöntem olarak kullanılabileceğini göstermektedir.

Keywords

• EEG
• beyin sinyali
• görsel uyaran
• şekil tahmini

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