Artificial intelligence for affective computing: an emotion recognition case study

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).