Classification of Four-Class Motor Imaginary EEG Signals with Deep Learning Using Empirical Mode Decomposition and Welch Method

Abstract

In electroencephalogram (EEG) based brain-computer interface (BCI) applications, it is very important to extract features from motor imagery (MI) signals obtained by imagining related limb movements and to classify them. Many different feature extraction methods and classification algorithms have been used in studies on MI-EEG signals. However, significant differences have been observed between the classification accuracies obtained as the number of classes increased in these signals. In the proposed method, feature extraction method including power spectral density (PSD) information of signals is presented. By applying empirical mode decomposition (EMD) to the raw EEG data, signals at different frequency levels were obtained. The PSD values of these signals were calculated using the welch method. The PSD values obtained were combined in a feature vector. Using the generated feature vectors, the long-short term memory (LSTM) network, a popular deep learning algorithm, was trained. The comparisons of the test accuracies obtained as a result of the training on the basis of individuals and channels were made in detail. As a result of the comparison, it was observed that the channels at the center of the scalp are more successful than the other channels.

Keywords

• Empirical Mode Decomposition
• Brain-Computer Interface
• Long-Short Term Memory
• Welch Method.

Ampirik Mod Ayrıştırması ve Welch Yöntemini Kullanarak Dört Sınıflı Motor Hayali EEG Sinyallerinin Derin Öğrenme ile Sınıflandırılması

Abstract

Elektroensefalogram (EEG) tabanlı beyin-bilgisayar arayüzü (BBA) uygulamalarında, kişilerin ilgili uzuv hareketlerini hayal etmesiyle elde edilen motor hayali (MI) sinyallerinden özellik çıkarmak ve bunları sınıflandırmak oldukça önemli bir konudur. MI-EEG sinyalleriyle ilgili yapılan çalışmalarda, birçok farklı özellik çıkarma yöntemleri ve sınıflandırma algoritmaları kullanılmıştır. Fakat bu sinyallerde sınıf sayısı arttıkça elde edilen sınıflandırma başarıları arasında belirgin farklar gözlemlenmiştir. Önerilen yöntemde, sinyallerin güç spektral yoğunluğu (PSD) bilgilerini içeren özellik çıkarma yöntemi sunulmuştur. Ham EEG verilerine ampirik mod ayrıştırması (EMD) uygulanarak farklı frekans seviyelerindeki sinyaller elde edilmiştir. Bu sinyallerin PSD değerleri welch yöntemi kullanılarak hesaplanmıştır. Elde edilen PSD değerleri bir öznitelik vektöründe birleştirilmiştir. Oluşturulan öznitelik vektörlerini kullanarak, popüler bir derin öğrenme algoritması olan uzun-kısa dönem hafıza (LSTM) ağı eğitilmiştir. Eğitim sonucunda elde edilen test başarılarının, kişiler ve kanallar bazındaki karşılaştırmaları detaylı olarak yapılmıştır. Karşılaştırma sonucunda kafa derisinin merkez noktasında bulunan kanalların, diğer kanallara göre daha başarılı oldukları görülmüştür.

Keywords

• Ampirik Mod Ayrıştırması
• Beyin-Bilgisayar Arayüzü
• Uzun-Kısa Dönem Hafıza
• Welch Yöntemi.

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