Classification of EEG signals by using support vector machines

K. Sercan Bayram; M. Ayyuce Kizrak; Bulent Bolat

doi:10.1109/INISTA.2013.6577636

Classification of EEG signals by using support vector machines

K. Sercan Bayram, M. Ayyuce Kizrak, Bulent Bolat

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Citations (Scopus)

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.

Original language	English
Title of host publication	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
DOIs	https://doi.org/10.1109/INISTA.2013.6577636
Publication status	Published - 2013
Externally published	Yes
Event	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 - Albena, Bulgaria Duration: 19 Jun 2013 → 21 Jun 2013

Publication series

Name	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

Conference

Conference	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
Country/Territory	Bulgaria
City	Albena
Period	19/06/13 → 21/06/13

Keywords

EEG
feature selection
suport vector machines

Access to Document

10.1109/INISTA.2013.6577636

Cite this

Bayram, K. S., Kizrak, M. A., & Bolat, B. (2013). Classification of EEG signals by using support vector machines. In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 Article 6577636 (2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013). https://doi.org/10.1109/INISTA.2013.6577636

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title = "Classification of EEG signals by using support vector machines",

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 = "EEG, feature selection, suport vector machines",

author = "Bayram, {K. Sercan} and Kizrak, {M. Ayyuce} and Bulent Bolat",

year = "2013",

doi = "10.1109/INISTA.2013.6577636",

language = "English",

isbn = "9781479906611",

series = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013",

booktitle = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013",

note = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 ; Conference date: 19-06-2013 Through 21-06-2013",

}

Bayram, KS, Kizrak, MA & Bolat, B 2013, Classification of EEG signals by using support vector machines. in 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013., 6577636, 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013, 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013, Albena, Bulgaria, 19/06/13. https://doi.org/10.1109/INISTA.2013.6577636

Classification of EEG signals by using support vector machines. / Bayram, K. Sercan; Kizrak, M. Ayyuce; Bolat, Bulent.
2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013. 2013. 6577636 (2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of EEG signals by using support vector machines

AU - Bayram, K. Sercan

AU - Kizrak, M. Ayyuce

AU - Bolat, Bulent

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

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KW - feature selection

KW - suport vector machines

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U2 - 10.1109/INISTA.2013.6577636

DO - 10.1109/INISTA.2013.6577636

M3 - Conference contribution

AN - SCOPUS:84883404053

SN - 9781479906611

T3 - 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

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T2 - 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

Y2 - 19 June 2013 through 21 June 2013

ER -

Classification of EEG signals by using support vector machines

Abstract

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Keywords

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