A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN

Panayu Keelawat, Nattapong Thammasan, Masayuki Numao, Boonserm Kijsirikul*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Downloads (Pure)

Abstract

Emotion recognition based on electroencephalograms has become an active research area. Yet, identifying emotions using only brainwaves is still very challenging, especially the subject-independent task. Numerous studies have tried to propose methods to recognize emotions, including machine learning techniques like convolutional neural network (CNN). Since CNN has shown its potential in generalization to unseen subjects, manipulating CNN hyperparameters like the window size and electrode order might be beneficial. To our knowledge, this is the first work that extensively observed the parameter selection effect on the CNN. The temporal information in distinct window sizes was found to significantly affect the recognition performance, and CNN was found to be more responsive to changing window sizes than the support vector machine. Classifying the arousal achieved the best performance with a window size of ten seconds, obtaining 56.85% accuracy and a Matthews correlation coefficient (MCC) of 0.1369. Valence recognition had the best performance with a window length of eight seconds at 73.34% accuracy and an MCC value of 0.4669. Spatial information from varying the electrode orders had a small effect on the classification. Overall, valence results had a much more superior performance than arousal results, which were, perhaps, influenced by features related to brain activity asymmetry between the left and right hemispheres.
Original languageEnglish
Number of pages19
JournalSensors
Volume21
Issue number5
DOIs
Publication statusPublished - 1 Mar 2021

Keywords

  • Emotion recognition
  • EEG
  • Machine learning
  • CNN
  • Spatiotemporal data
  • Brainwave
  • Neuroscience
  • Window size
  • Electrode order

Fingerprint Dive into the research topics of 'A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN'. Together they form a unique fingerprint.

Cite this