Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework

Obada Al Zoubi, Mariette Award, Nikola Kasabov

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


Recent technological advances in machine learning offer the possibility of decoding complex data sets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data. LSM were applied to a previously validated EEG dataset where subjects view a battery of emotional film clips and then rate their degree of emotion during each film based on valence, arousal, and liking levels. We introduce LSM as a model for an automatic feature extraction and prediction from raw EEG with potential extension to a wider range of applications. We also elaborate on how to exploit the separation property in LSM to build a multipurpose and anytime recognition framework, where we used one trained model to predict valence, arousal and liking level sat different durations of the input. Our simulations showed that the LSM-based framework achieve out-standing results in comparison with other works using different emotion prediction scenarios with cross validation.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalArtificial Intelligence in Medicine
Early online date1 Feb 2018
Publication statusPublished (in print/issue) - 31 Mar 2018


  • Emotion recognition
  • EEG
  • Liquid state machine
  • Machine learning
  • pattern recognition
  • Feature extraction


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