Classification of Eeg Signals Using Transfer Learning on Convolutional Neural Networks via Spectrogram


Top A. E.

Ankara Yıldırım Beyazıt Üniversitesi Fen Bilimleri Enstitüsü, Ankara, 2018

  • Publication Type: Book / Other Book
  • Publication Date: 2018
  • Publisher: Ankara Yıldırım Beyazıt Üniversitesi Fen Bilimleri Enstitüsü
  • City: Ankara

Abstract

Previous studies on classifying Electroencephalography (EEG) sleep data generally use the signal itself. Many of these studies need series of pre-processing operations, manual feature extraction, complex and hard application processes. There is no need for lots of pre-processing stages in Convolutional Neural Networks (CNN) and features can be learned automatically instead of using manually extracted features. Also CNNs have better performance over most of other methods in visual classification. The study presented in this thesis is based on applying transfer learning with CNNs via spectrogram images, that were obtained by using Short-Time Fourier Transform (STFT), on ISRUC-Sleep dataset (ISRUC) for classifying EEG signals. AlexNet trained with 100 patient subjects from ISRUC was used as pre-trained CNN. For classifying sleep stages, single-channel EEG data, that was taken from 10 healthy people, was used as target domain. To reduce overfitting, an image translation operation was employed and images were augmented horizontally. The main purpose of using transfer learning method in this study is achieving better training duration and accuracy. Applying transfer learning increased the accuracy of the classification by 3.11 percent, when compared to the result of using non-pretrained AlexNet, which was trained from scratch. When the source domain of transfer learning was ImageNet, the accuracy was decreased by 2.73 percent compared to the result of training from scratch. Results showed that transfer learning increases the accuracy when target and source domains are similar, but it may decrease the accuracy when used on different domains (i.e., ImageNet is an image dataset and ISRUC consists of signals). In addition to the study, scalogram images from Continuous Wavelet Transform (CWT) were also tested.