Open AccessComparison of Dimension Reduction Algorithms on EEG Signals
Author(s) -
Umit Sandikcioglu,
Ayten Atasoy,
Yavuz Kablan,
Yusuf Sevim,
Murat Aykut
Publication year - 2018
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.201812043
Subject(s) - principal component analysis , dimensionality reduction , pattern recognition (psychology) , feature extraction , dimension (graph theory) , kernel principal component analysis , computer science , artificial intelligence , kernel (algebra) , matrix (chemical analysis) , reduction (mathematics) , data set , algorithm , feature (linguistics) , independent component analysis , locality , mathematics , support vector machine , kernel method , linguistics , materials science , geometry , philosophy , combinatorics , pure mathematics , composite material
Like in all classification applications, the most important process which increases classification success of electroencephalography (EEG) applications is to choose the proper features for signals. Since there is not certain feature extraction method for data classification applications, used feature matrix size can be redundantly large and this state effect the system's speed and success negatively. In this study Data Set III of BCI competition 2003 was used. We extract features using this data set and then dimension of feature matrix size reduced by using Principal Component Analysis, Kernel Principal Component Analysis and Locality Preserving Projections method which is alternative to Principal Component Analysis. As a result, the best success rate is obtained as 83.28% when Linearity Preserving Projections algorithm with Chebycev distance measuring method is used.