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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform

Ampirik Dalgacık Dönüşümünde Zaman-Frekans Özniteliklerini Kullanarak EEG tabanlı Uzamsal Dikkat Kayması Tespiti

How to cite: Altan G, İnat G. Eeg based spatial attention shifts detection using time-frequency features on empirical wavelet transform. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2021; 4(2): 144-149. DOI: 10.54856/jiswa.202112181

Full Text: PDF, in English.

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Title: EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform

Abstract: The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.

Keywords: electroencephalogram (EEG); empirical wavelet transform; EWT; k-NN; SVM; MLP; decision tree


Başlık: Ampirik Dalgacık Dönüşümünde Zaman-Frekans Özniteliklerini Kullanarak EEG tabanlı Uzamsal Dikkat Kayması Tespiti

Özet: İnsan sinir sistemi, çoğunluğu beyinde bulunan 100 milyardan fazla sinir hücresine sahiptir. Elektriksel değişiklikler, Elektroensefalogram (EEG), bu sinirlerin etkileşimi yoluyla meydana gelir. EEG, olaylarla ilgili potansiyelleri, düşünsel motor görevleri, nörolojik rahatsızlıkları, mekansal dikkat kaymalarını ve daha birçok durumu değerlendirmek için kullanılır. Bu çalışmada, 18 sağlıklı bireyden alınan 29-kanallı EEG kayıtları ile analizler gerçekleştirilmiştir. Her kayıt, öznitelik çıkarma aşamasında bir zaman-frekans alanı analiz tekniği olan Ampirik Dalgacık Dönüşümü kullanılarak ayrıştırılmıştır. Sonrasında, ayrıştırılan her modülasyondan hesaplanan istatistiksel öznitelikler, geleneksel makine öğrenme algoritmalarının beslenmesinde kullanılmıştır. Önerilen model, Karar Ağacı algoritmasını kullanarak %89.24 oranıyla en iyi uzamsal dikkat kayması algılama başarısını elde etmiştir.

Anahtar kelimeler: Elektroensefalografi (EEG); ampirik dalgacık dönüşümü; ADA; k-NN; DVM; MLP; karar ağacı


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