Analysis of brain signals constitute an importance,
especially for paralyzed people or people suffer from motor disabilities. For
this aim, some studies have been evaluated to measure signals from the scalp to
provide non-muscle control arguments. Brain-Computer Interface Systems turns
these signals into device signals that are controllable at the level of
thought. In this paper, we classify diverse tasks according to EEG
(electroencephalogram) signals. Then pre-processing, feature extraction and
classification steps are hold. For classification, we use FLDA, Linear SVM,
Quadratic SVM, PCA, and k-NN methods. The best result is obtained by using
k-NN.
Analysis of brain signals constitute an importance,
especially for paralyzed people or people suffer from motor disabilities. For
this aim, some studies have been evaluated to measure signals from the scalp to
provide non-muscle control arguments. Brain-Computer Interface Systems turns
these signals into device signals that are controllable at the level of
thought. In this paper, we classify diverse tasks according to EEG
(electroencephalogram) signals. Then pre-processing, feature extraction and
classification steps are hold. For classification, we use FLDA, Linear SVM,
Quadratic SVM, PCA, and k-NN methods. The best result is obtained by using
k-NN.
Primary Language | English |
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Subjects | Engineering |
Journal Section | Articles |
Authors | |
Publication Date | November 30, 2018 |
Submission Date | August 15, 2018 |
Published in Issue | Year 2018 Volume: 8 Issue: 2 |