Abstract
AbstractA recent technology which makes possible for us to interact with automated systems without using any body part is called Brain Computer Interface (BCI). In its concrete applications, electroencephalogram (EEG) is benefited by a BCI environment for being capable of obtaining brain waves. In our study, evaluation of success rates of the predictions made by C x k - Nearest Neighborhood (Cxk-NN) Algorithm for EEG Eye State Data whose states are called “Opened Eye“ and “Closed Eye“ is applied. This EEG Eye State dataset is obtained from UCI Machine Learning Repository on the web and it is a highly-used benchmark data on this field. As there are only two classes of the signals, we test binary classification performance of our classification algorithm (Cxk –NN). Comparison of those values with the ones obtained by the other successful classification algorithms in the literature applied on the same data set also take place in our study. Cxk-NN is an instance-based classification method advanced from simple k – Nearest Neighborhood Algorithm, and improved success results are observed when it is compared with k-NN.
 Keywords: brain computer interface (bci), classificaiton, eeg, c x k - nearest neighborhood algorihtm.
Highlights
The brain which is the center of decision and balance has a complicated structure
The aim of this research we made is to demonstrate that C x K – Nearest Algorithm can make a more successful classification than k-Nearest Neighborhood Algorithm, which is a basic algorithm, when classifying binary EEG signals
The C x K – Nearest Neighborhood approach is an improved version of the well-known k- Nearest Neighborhood (KNN) algorithm
Summary
The brain which is the center of decision and balance has a complicated structure. Especially, human brain has a more difficult to understand structure than other animal brains. The idea of being able to read those thoughts as inputs is based on the emergence of electrical signals in some parts of the human brain when a person imagines something in his mind. Those types of signals should be recognized in an electronic environment. These systems are quite beneficial especially for patients of ALS and people suffering from some sort of strokes others (He, 2005; Schalk, McFarland, Hinterberger, Birbaumer & Wolpaw, 2016; Wolpaw, Birbaumer, Heetderks, McFarland, Peckham, Schalk & Vaughan, 2000). Brain signals are taken over hair skin since it does not cause any pain on the subject others (He, 2005)
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