Yıl 2021, Cilt 5 , Sayı 1, Sayfalar 1 - 6 2021-06-30

The Role of Feature Selection in Significant Information Extraction from EEG Signals
The Role of Feature Selection in Significant Information Extraction from EEG Signals

Eda DAĞDEVİR [1] , Mahmut TOKMAKÇI [2]


Information extraction from EEG signals for use in Brain Machine Interface systems has been a highly effective research topic recently. Due to the complexity, high dimension, and subject specific behavior of the EEG signals make feature extraction and selection very important. For this reason, there are many studies in the direction of feature extraction and selection which affect the performance of the Brain Machine Interface system at a high level. In this study, different statistical characteristics were obtained from wavelet coefficients obtained by wavelet transform by using BCI Competition IV-2b data set. The selection of the efficient ones of these features is provided by Principal Component Analysis. The fitness of logistic regression model established with both feature groups was measured by Akaike Information Criteria. The results indicated that relatively better statistical performance can be obtained by using fewer features thanks to PCA. These results are important in terms of statistical comparison and demonstration of the success in extracting information from EEG signals.

Information extraction from EEG signals for use in Brain Machine Interface systems has been a highly effective research topic recently. Due to the complexity, high dimension, and subject specific behavior of the EEG signals make feature extraction and selection very important. For this reason, there are many studies in the direction of feature extraction and selection which affect the performance of the Brain Machine Interface system at a high level. In this study, different statistical characteristics were obtained from wavelet coefficients obtained by wavelet transform by using BCI Competition IV-2b data set. The selection of the efficient ones of these features is provided by Principal Component Analysis. The fitness of logistic regression model established with both feature groups was measured by Akaike Information Criteria. The results indicated that relatively better statistical performance can be obtained by using fewer features thanks to PCA. These results are important in terms of statistical comparison and demonstration of the success in extracting information from EEG signals.
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Birincil Dil en
Konular Mühendislik, Mühendislik, Elektrik ve Elektronik, Mühendislik, Ortak Disiplinler
Bölüm Makaleler
Yazarlar

Orcid: 0000-0001-7065-9829
Yazar: Eda DAĞDEVİR
Kurum: ERCIYES UNIVERSITY
Ülke: Turkey


Yazar: Mahmut TOKMAKÇI (Sorumlu Yazar)
Kurum: ERCİYES ÜNİVERSİTESİ
Ülke: Turkey


Destekleyen Kurum Erciyes University
Proje Numarası FDK-2020-9876
Teşekkür The authors acknowledge the financial, laboratory and infrastructure support provided by the Scientific Research Projects Coordination Unit, Erciyes University, Turkey (FDK-2020-9876).
Tarihler

Yayımlanma Tarihi : 30 Haziran 2021

Bibtex @araştırma makalesi { bilmes845452, journal = {International Scientific and Vocational Studies Journal}, issn = {2618-5938}, address = {Gaziosmanpaşa Üni. Taşlıçiftlik kampüsü Teknopark binası No:111 Tokat/Merkez}, publisher = {Umut SARAY}, year = {2021}, volume = {5}, pages = {1 - 6}, doi = {}, title = {The Role of Feature Selection in Significant Information Extraction from EEG Signals}, key = {cite}, author = {Dağdevir, Eda and Tokmakçı, Mahmut} }
APA Dağdevir, E , Tokmakçı, M . (2021). The Role of Feature Selection in Significant Information Extraction from EEG Signals . International Scientific and Vocational Studies Journal , 5 (1) , 1-6 . Retrieved from https://dergipark.org.tr/tr/pub/bilmes/issue/63451/845452
MLA Dağdevir, E , Tokmakçı, M . "The Role of Feature Selection in Significant Information Extraction from EEG Signals" . International Scientific and Vocational Studies Journal 5 (2021 ): 1-6 <https://dergipark.org.tr/tr/pub/bilmes/issue/63451/845452>
Chicago Dağdevir, E , Tokmakçı, M . "The Role of Feature Selection in Significant Information Extraction from EEG Signals". International Scientific and Vocational Studies Journal 5 (2021 ): 1-6
RIS TY - JOUR T1 - The Role of Feature Selection in Significant Information Extraction from EEG Signals AU - Eda Dağdevir , Mahmut Tokmakçı Y1 - 2021 PY - 2021 N1 - DO - T2 - International Scientific and Vocational Studies Journal JF - Journal JO - JOR SP - 1 EP - 6 VL - 5 IS - 1 SN - 2618-5938- M3 - UR - Y2 - 2021 ER -
EndNote %0 International Scientific and Vocational Studies Journal The Role of Feature Selection in Significant Information Extraction from EEG Signals %A Eda Dağdevir , Mahmut Tokmakçı %T The Role of Feature Selection in Significant Information Extraction from EEG Signals %D 2021 %J International Scientific and Vocational Studies Journal %P 2618-5938- %V 5 %N 1 %R %U
ISNAD Dağdevir, Eda , Tokmakçı, Mahmut . "The Role of Feature Selection in Significant Information Extraction from EEG Signals". International Scientific and Vocational Studies Journal 5 / 1 (Haziran 2021): 1-6 .
AMA Dağdevir E , Tokmakçı M . The Role of Feature Selection in Significant Information Extraction from EEG Signals. BİLMES DERGİ, ISVOS. 2021; 5(1): 1-6.
Vancouver Dağdevir E , Tokmakçı M . The Role of Feature Selection in Significant Information Extraction from EEG Signals. International Scientific and Vocational Studies Journal. 2021; 5(1): 1-6.
IEEE E. Dağdevir ve M. Tokmakçı , "The Role of Feature Selection in Significant Information Extraction from EEG Signals", International Scientific and Vocational Studies Journal, c. 5, sayı. 1, ss. 1-6, Haz. 2021