Title :
Classification of EEG signals by using support vector machines
Author :
Bayram, K. Sercan ; Kizrak, M. Ayyuce ; Bolat, B.
Author_Institution :
Electr. & Electron. Eng. Dept., Halic Univ., Istanbul, Turkey
Abstract :
In this work, EEG signals were classified by support vector machines to detect whether a subject´s planning to perform a task or not. Various different kernels were utilized to find the best kernel function and after that, a feature selection process was realized. The results are comparable to the recent works.
Keywords :
electroencephalography; medical signal processing; signal classification; support vector machines; EEG signal classification; feature selection process; kernel function; planning; support vector machines; Accuracy; Band-pass filters; Classification algorithms; Electroencephalography; Kernel; Planning; Support vector machines; EEG; feature selection; suport vector machines;
Conference_Titel :
Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on
Conference_Location :
Albena
Print_ISBN :
978-1-4799-0659-8
DOI :
10.1109/INISTA.2013.6577636
