A study on the effect of psychophysiological signal features on classification methods
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The most important factor affecting the performance of a BCI (Brain Cothputer Interface) systems, is classification feature set. Choosing the right features to increase the success of classification is the key point. In BCI systems, signals from brain are used to store into dataset. In this study, BCI Competition III dataset 1 consisting of ECoG (Electrocorticography) signals is preferred. In the first part, in order to decrease the processing load, the number of channels are reduced by eliminating Channels (electrodes) which have low separation success. We developed new algorithm ADA (Arc Detection Algorithm) based on visual channel selection to determine quickly optimal channel subset. Than Obtained Wavelet coefficients by Discrete Wavelet Transform (DWT) and determined classification features from Wavelet coefficients. These features are used to classify by KNN (K Nearest Neighbors), SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis) by different feature set combination. The classification successes of feature combinations which are used in classification are compared. The impact on the classification performance of the right channel and the right property choice is observed. Test results are made with different frequency bands are compared with the same feature set. As a result, the highest classification accuracy of 95% was obtained by selected channels and feature. (C) 2017 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
ECoG, Discrete Wavelet Transform (DWT), Classification
Kaynak
Measurement
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
101
