Unsupervised feature learning for EEG-based emotion recognition

Zirui Lan, Olga Sourina, Lipo Wang, Reinhold Scherer, Gernot Müller-Putz

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Spectral band power features are one of the most widely used features in the studies of electroencephalogram (EEG)-based emotion recognition. The power spectral density of EEG signals is partitioned into different bands such as delta, theta, alpha and beta band etc. Though based on neuroscientific findings, the partition of frequency bands is somewhat on an ad-hoc basis, and the definition of frequency ranges of the bands of interest can vary between studies. On the other hand, it is also arguable that one definition of power bands could perform equally well on all subjects. In this paper, we propose to use autoencoder to automatically learn from each subject the salient frequency components from power spectral density estimated as periodogram by Fast Fourier Transform (FFT). We propose a network architecture especially for EEG feature extraction, one that adopts hidden unit clustering with added pooling neuron per cluster. The classification accuracy with features extracted by our proposed method is benchmarked against that with standard power features. Experimental results show that our proposed feature extraction method achieves accuracy ranging from 44% to 59% for three-emotion classification. We also see a 4-20% accuracy improvement over standard band power features.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Cyberworlds, CW 2017 - in cooperation with
Subtitle of host publicationEurographics Association International Federation for Information Processing ACM SIGGRAPH
PublisherInstitute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781538620892
Publication statusPublished - 27 Nov 2017
Event2017 International Conference on Cyberworlds: CW 2017 - Chester, United Kingdom
Duration: 20 Sept 201722 Sept 2017


Conference2017 International Conference on Cyberworlds
Abbreviated titleCYBERWORLDS
Country/TerritoryUnited Kingdom
Internet address


  • Autoencoder
  • Brain-computer-interface (BCI)
  • Electroencephalogram (EEG)
  • Emotion classification
  • Power spectral density
  • Unsupervised feature extraction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Media Technology
  • Modelling and Simulation


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