Abstract
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 language | British English |
|---|---|
| Title of host publication | Emotion Recognition |
| Subtitle of host publication | A Pattern Analysis Approach |
| Publisher | wiley |
| Pages | 269-293 |
| Number of pages | 25 |
| ISBN (Electronic) | 9781118910566 |
| ISBN (Print) | 9781118130667 |
| DOIs | |
| State | Published - 2 Jan 2015 |
Keywords
- Brain activity
- EEG-based Emotion Recognition (EEG-ER)
- Information analysis
- Neuron system
- Signal processing tools