Investigation Of Feature Selection Algorithms On A Cognitive Task Classification: A Comparison Study

Yıl 2018, Cilt: 6 Sayı: 2, 99 - 104, 30.04.2018

Server Göksel Eraldemir , Mustafa Turan Arslan , Esen Yıldırım

https://doi.org/10.17694/bajece.419549

Cited By: 2

Öz

In this study, the effects of feature selection on classification of
the electrical signals generated in the brain during numerical and verbal
operations are investigated. 18 healthy university/college students were chosen
for the experimental study. EEG signals were recorded during silent reading and
mental arithmetic operations without using any pen and paper. A total of 60
slides, 30 of which contained reading passages and the rest contained
arithmetic operations, were presented in the experiment.  EEG signals recorded from 26 channels during
the slide show. The recorded EEG signals were analyzed by Hilbert Huang
Transform (HHT), and then features were extracted. 312 features were classified
by Bayesian Network algorithm without applying feature selection with 92.60%
average accuracy. Consistency measures and Correlation based Feature Selection
methods were, then, used for feature selection and the numbers of selected
features are 8 and 39 on average, respectively. Classification accuracies by
using these feature selection algorithms were obtained as 93.98% and 95.58%,
respectively. The results showed that feature selection algorithms contribute
positively to the classification performance.

Anahtar Kelimeler

Hilbert Huang Transform, Consistency Measures, Correlation based Feature Selection, EEG Classification

Kaynakça

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