Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information
This work addresses emotion recognition for human-centric services, representing an incremental improvement in accuracy.
The authors tackled EEG-based emotion recognition by proposing a CNN approach that incorporates brain connectivity features and asymmetric brain activity patterns, achieving improved recognition accuracy.
Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in previous studies, which can account for synchronous activations of different brain regions. In addition, we develop a method to effectively capture asymmetric brain activity patterns that are important for emotion recognition. Experimental results confirm the effectiveness of our approach.