Emotion Classification from EEG Signals in Convolutional Neural Networks

Donmez H., ÖZKURT N.

Innovations in Intelligent Systems and Applications Conference (ASYU), İzmir, Turkey, 31 October - 02 November 2019, pp.104-109, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/asyu48272.2019.8946364
  • City: İzmir
  • Country: Turkey
  • Page Numbers: pp.104-109
  • Keywords: EEG, CNN, Deep Learning, Emotion Classification
  • Dokuz Eylül University Affiliated: No

Abstract

The objective of this research is to classify EEG (electroencephalography) signal recordings of the subjects evoked by visual stimulus by using CNN (Convolutional Neural Networks). EEG records the electrical activity of brain signals. In medicine, EEG is used to diagnose some neurological disorders but moreover the classification of the emotions is also possible from EEG recordings. Emotion recognition is an important task for the computers in machine perception. Therefore, in this study the participants are presented with a video containing funny, scary and sad excerpts and simultaneously EEG signal is measured by Neurosky Mindwave EEG Headset. The spectrogram of EEG signals is supplied to CNN and three emotions are classified using brain signal spectrogram images.