Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features
This work addresses the challenge of adapting EEG-based emotion recognition across different individuals, which is incremental for enhancing affective human-computer interaction systems.
The paper tackled cross-individual emotion recognition from EEG data by proposing a dynamic entropy-based pattern learning (DEPL) framework, which improved classification accuracy over traditional methods on public databases like DEAP and MAHNOB-HCI.
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.