  Use of the electroencephalogram (EEG) and machine learning approaches to
recognize emotions can facilitate affective human computer interactions.
However, the type of EEG data constitutes an obstacle for cross-individual EEG
feature modelling and classification. To address this issue, we propose a
deep-learning framework denoted as a dynamic entropy-based pattern learning
(DEPL) to abstract informative indicators pertaining to the neurophysiological
features among multiple individuals. DEPL enhanced the capability of
representations generated by a deep convolutional neural network by modelling
the interdependencies between the cortical locations of dynamical entropy based
features. The effectiveness of the DEPL has been validated with two public
databases, commonly referred to as the DEAP and MAHNOB-HCI multimodal tagging
databases. Specifically, the leave one subject out training and testing
paradigm has been applied. Numerous experiments on EEG emotion recognition
demonstrate that the proposed DEPL is superior to those traditional machine
learning (ML) methods, and could learn between electrode dependencies w.r.t.
different emotions, which is meaningful for developing the effective
human-computer interaction systems by adapting to human emotions in the real
world applications.
