Artificial intelligence for affective computing: an emotion recognition case study

Pablo Arnau-González*, Stamos Katsigiannis, Miguel Arevalillo-Herráez, Naeem Ramzan

*Corresponding author for this work

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


This chapter provides an introduction on the benefits of artificial intelligence techniques for the field of affective computing, through a case study about emotion recognition via brain (electroencephalography - EEG) signals. Readers are first provided with a general description of the field, followed by the main models of human affect, with special emphasis to Russell's Circumplex model and the Pleasure-Arousal-Dominance (PAD) model. Finally, an AI-based method for the detection of affect elicited via multimedia stimuli is presented. The method combines both connectivity-based and channel-based EEG features with a selection method that considerably reduces the dimensionality of the data and allows for efficient classification. In particular, the Relative Energy (RE) and its logarithm in the spatial domain, as well as the spectral power (SP) in the frequency domain are computed for the four typically used EEG frequency bands (α, β, γ and θ), and complemented with the mutual information measured over all EEG channel pairs. The resulting features are then reduced by using a hybrid method that combines supervised and unsupervised feature selection. Detection results are compared to state-of-the-art methods on the DEAP benchmarking dataset for emotion analysis, which is composed of labelled EEG recordings from 32 individuals, acquired while watching 40 music videos. The acquired results demonstrate the potential of AI-based methods for emotion recognition, an application that can significantly benefit the fields of human-computer interaction (HCI) and of quality-of-experience (QoE).
Original languageEnglish
Title of host publicationAI for Emerging Verticals
Subtitle of host publicationHuman-Robot Computing, Sensing and Networking
EditorsMuhammad Zeeshan Shakir, Naeem Ramzan
ISBN (Electronic)9781785619830
ISBN (Print)9781785619823
Publication statusPublished - 30 Nov 2020


  • artificial intelligence
  • affective computing
  • emotion recognition
  • case study


Dive into the research topics of 'Artificial intelligence for affective computing: an emotion recognition case study'. Together they form a unique fingerprint.

Cite this