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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.

Classification of Multi-Class Motor Imaginary Tasks using Poincare Measurements Extracted from EEG Signals

EEG Sinyallerinden Çıkarılan Poincare Ölçümlerini Kullanarak Çok Sınıflı Motor Hayali Görevlerin Sınıflandırılması

How to cite: Değirmenci M, Yüce YK, İşler Y. Classification of multi-class motor imaginary tasks using poincare measurements extracted from eeg signals. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2022; 5(2): 74-78. DOI: 10.54856/jiswa.202212204

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Title: Classification of Multi-Class Motor Imaginary Tasks using Poincare Measurements Extracted from EEG Signals

Abstract: Motor Imaginary (MI) electroencephalography (EEG) signals are generated with the recording of brain activities when a participant imagines a movement without physically performing it. The correct decoding of MI signals have been became an important task due to the application of these signals in the rehabilitation process of paralyzed patients in recent studies. However, the decoding of the these signals is still an evolving challenge in the design of a brain-computer interface (BCI) system. In this study, a machine learning based approach using Poincare measurements from non-linear measurements of MI EEG signals is proposed for classification of four-class MI tasks. The m-lagged Poincare plots were used to extract non-linear features and m is set to be values from 1 to 10. The performances of feature vectors which are extracted from 10 lag values and feature vector which is the combinations of these vectors were investigated separately in experimental evaluation section. The 24 different typical classification algorithms were tested in differentiating MI tasks using 5-fold cross-validation. Each of the these algorithms tested 10 times to analyzed the repeatability of the experimental results. The highest classifier performance of 47.08% among these 11 feature vectors was achieved over the combination feature vector that includes all lag values features using Quadratic Support Vector Machine (SVM). According to average accuracy value of 24 classifiers in 11 feature vector, the most discriminative feature set is 9th vector that consists of features extracted when lag value defined as 9. As a result, the innovative aspect of this study is the application of Poincare plots, one of the nonlinear feature extraction methods, in motor imaginary task classification.

Keywords: Brain-computer interface; EEG signals; Machine learning; Motor imaginary task classification; Poincare plot


Başlık: EEG Sinyallerinden Çıkarılan Poincare Ölçümlerini Kullanarak Çok Sınıflı Motor Hayali Görevlerin Sınıflandırılması

Özet: Motor Hayali (MH) elektroensefalografi (EEG) sinyalleri, bir katılımcı fiziksel olarak gerçekleştirmeden bir hareketi hayal ettiğinde beyin aktivitelerinin kaydedilmesiyle üretilir. Son yıllarda yapılan çalışmalarda bu sinyallerin felçli hastaların rehabilitasyon sürecinde uygulanması nedeniyle MH EEG sinyallerinin doğru çözümlenmesi önemli bir görev haline gelmiştir. Bununla birlikte, bu sinyallerin kodunun çözülmesi, bir beyin-bilgisayar arayüzü (BBA) sisteminin tasarımında hala gelişen bir zorluktur. Bu çalışmada, dört sınıflı MH görevlerinin sınıflandırılması için MH EEG sinyallerinin doğrusal olmayan ölçümlerinden Poincare ölçümlerini kullanan makine öğrenmesi tabanlı bir yaklaşım önerilmiştir. M-gecikmeli Poincare grafikleri, doğrusal olmayan öznitelikleri çıkarmak için kullanıldı ve m, 1'den 10'a kadar olan değerler olacak şekilde ayarlandı. 10 gecikme değerinden elde edilen öznitelik vektörleri ile bu vektörlerin birleşimi olan öznitelik vektörünün performansları deneysel değerlendirme bölümünde ayrı ayrı incelenmiştir. 24 farklı tipik sınıflandırma algoritması, 5 kat çapraz doğrulama kullanılarak MI görevlerinin ayırt edilmesinde test edilmiştir. Bu algoritmaların her biri, deneysel sonuçların tekrarlanabilirliğini analiz etmek için 10 defa test edildi. Bu 11 öznitelik vektörü arasında 47,08% ile en yüksek sınıflandırıcı performansı, Kuadratik Destek Vektör Makinesi (DVM) kullanılarak tüm gecikme değerleri özniteliklerini içeren kombinasyon öznitelik vektörü üzerinden elde edilmiştir. 11 öznitelik vektöründe 24 sınıflandırıcının ortalama doğruluk değerine göre en ayırt edici öznitelik seti, gecikme değeri 9 olarak tanımlandığında çıkarılan özniteliklerden oluşan 9. vektördür. Sonuç olarak, bu çalışmanın yenilikçi yönü, doğrusal olmayan öznitelik çıkarma yöntemlerinden biri olan Poincare çizimlerinin motor hayali görev sınıflandırmasında uygulanmasıdır.

Anahtar kelimeler: Beyin-bilgisayar arayüzü; EEG sinyalleri; Makine öğrenmesi; Motor hayali görev sınıflandırması; Poincare çizimi


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