EEG-Based Emotion Recognition Using Advanced Signal Processing Techniques

Panagiotis C. Petrantonakis, Leontios J. Hadjileontiadis

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Scopus citations


This chapter, by employing advanced signal processing techniques, sheds light upon two main issues of the EEG-based Emotion Recognition (EEG-ER) field, that is, the aspect of quantitatively estimating the degree of emotion elicitation in subjects under suitable stimuli and the overall enhancement of the EEG-ER systems performance by introducing new feature vectors. In particular, the use of brain functional concepts, that is, the activation of the Mirror Neuron System and the asymmetry in the brain activity under emotion stimulation, to evaluate the emotion-related content of an EEG signal is described. This includes sophisticated signal processing tools with robust mathematical background, drawn from the field of multidimensional directed information analysis, resulting in an "emotion-based segmentation" of the related EEG data. In conclusion, the chapter points out the potential of the proposed approaches toward efficient EEG-ER systems and discusses some future directions of research for the EEG-ER task.

Original languageBritish English
Title of host publicationEmotion Recognition
Subtitle of host publicationA Pattern Analysis Approach
Number of pages25
ISBN (Electronic)9781118910566
ISBN (Print)9781118130667
StatePublished - 2 Jan 2015


  • Brain activity
  • EEG-based Emotion Recognition (EEG-ER)
  • Information analysis
  • Neuron system
  • Signal processing tools


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