EEG based emotional state estimation using 2-D deep learning technique

Ozdemir M. A., Degirmenci M., Guren O., Akan A.

2019 Medical Technologies Congress, TIPTEKNO 2019, İzmir, Turkey, 3 - 05 October 2019, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/tiptekno.2019.8895158
  • City: İzmir
  • Country: Turkey
  • Keywords: Convolutional Neural Network, EEG Images, Electroencephalogram, Emotion Detection Topographic EEG Maps
  • Kütahya Health Sciences University Affiliated: No

Abstract

Emotion detection is very crucial role on diagnosis of brain disorders and psychological disorders. Electroencephalogram (EEG) is useful tool that obtain complex physiological brain signals from human. In this paper, we proposed a novel approach for emotional state estimation using convolutional neural network (CNN) based deep learning technique from EEG signals. Firstly, we convert 32 lead EEG signals to 2D EEG images with Azimuthal Equidistant Projection (AEP) technique. Then, 2D images that represented measurements of EEG signals sent to CNN based deep neural network for classification. In this study, we have achieved accuracy of 95.96% two classes as negative and positive valence, 96.09% two classes as high and low arousal and 95.90% two classes as high and low arousal dominance.