Examining the Effect of Dimension Reduction on EEG Signals by KNearest Neighbors Algorithm

Abstract

Machine learning which a paradigm of methods that makes inferences from existing data usingmathematical and statistical methods and is inferred to be unknown. The proposed method in this paper,supervised learning algorithm is applied to EEG (electroencephalography) data and classification algorithmperformance is analyzed and results are examined in MATLAB. K-Nearest Neighbors Algorithm (k-NN) is usedin this paper as algorithm. This classification was evaluated in two stages, with and without PrincipalComponent Analysis (PCA). Dimension reduction is the process of reducing the size of dimension of the data.By reducing the size of the data set with PCA, it is expected to protect important data features. KNN has givenresults that can be regarded as prudent in terms of classification accuracy. The results of the present workshowed that appropriate features combined with classifier can be done significant classification for differentbioelectrical signal.