DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices

S. Katsigiannis, N. Ramzan

Research output: Contribution to journalArticlepeer-review

263 Citations (Scopus)
744 Downloads (Pure)


In this work, we present DREAMER, a multi-modal database consisting of electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect elicitation by means of audio-visual stimuli. Signals from 23 participants were recorded along with the participants self-assessment of their affective state after each stimuli, in terms of valence, arousal, and dominance. All the signals were captured using portable, wearable, wireless, low-cost and off-the-shelf equipment that has the potential to allow the use of affective computing methods in everyday applications. A baseline for participant-wise affect recognition using EEG and ECG -based features, as well as their fusion, was established through supervised classification experiments using Support Vector Machines (SVMs). The selfassessment of the participants was evaluated through comparison with the self-assessments from another study using the same audio-visual stimuli. Classification results for valence, arousal and dominance of the proposed database are comparable to the ones achieved for other databases that use non-portable, expensive, medical grade devices. These results indicate the prospects of using low-cost devices for affect recognition applications. The proposed database will be made publicly available in order to allow researchers to achieve a more thorough evaluation of the suitability of these capturing devices for affect recognition applications.
Original languageEnglish
Pages (from-to)98-107
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 27 Mar 2017


  • Databases
  • Electrocardiography
  • Electroencephalography
  • Emotion recognition
  • Multimedia communication
  • Physiology
  • Wireless communication
  • Affect
  • ECG
  • EEG
  • Emotion
  • affect recognition
  • physiological signals
  • wireless devices


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