A novel deep learning approach for classification of EEG motor imagery signals

TABAR Y. R., HALICI U.

JOURNAL OF NEURAL ENGINEERING, cilt.14, sa.1, 2017 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 1
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1088/1741-2560/14/1/016003
  • Dergi Adı: JOURNAL OF NEURAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: EEG, BCI, motor imagery, deep learning, convolutional neural networks, stacked autoencoders, BRAIN-COMPUTER INTERFACE, SINGLE-TRIAL EEG, COMMUNICATION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Objective. Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. Approach. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. Main results. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Significance. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.