Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification

Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain. ©2019 Walter de Gruyter GmbH, Berlin/Boston 2019.

Dergi Adı Biomedizinische Technik
Sayfalar -
Yayın Yılı 2019
Eser Adı
[dc.title]
Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification
Yazar
[dc.contributor.author]
Uyulan C.
Yazar
[dc.contributor.author]
Ergüzel T.T.
Yazar
[dc.contributor.author]
Tarhan N.
Yayın Yılı
[dc.date.issued]
2019
Yayıncı
[dc.publisher]
De Gruyter
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain. ©2019 Walter de Gruyter GmbH, Berlin/Boston 2019.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
electroencephalography
Konu Başlıkları
[dc.subject]
feature selection
Konu Başlıkları
[dc.subject]
wavelet entropy
Konu Başlıkları
[dc.subject]
wavelet families
Konu Başlıkları
[dc.subject]
wavelet packet transform
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0013-5585
Dergi Adı
[dc.relation.journal]
Biomedizinische Technik
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1515/bmt-2018-0105
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/5640
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
16
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
21 Şubat 2024 10:23
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mental entropy feature information classify biomarker functions results wavelet through subspaces measure decomposed accuracy collected different signal features signals periodic representation tracking Event-related series discrete function coupled classifier Fourier pattern finally tested Simulink platform demonstrate
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