EEG-based Emotional Video Classification via Learning Connectivity Structure
This work addresses the challenge of accurately classifying EEG signals for monitoring emotional states during video consumption, offering a novel approach to automate connectivity structure learning, though it is incremental in advancing EEG classification techniques.
The authors tackled the problem of EEG-based emotional video classification by proposing an end-to-end neural network that learns connectivity structures from raw EEG signals, achieving improved performance compared to existing methods using manually defined structures.
Electroencephalography (EEG) is a useful way to implicitly monitor the users perceptual state during multimedia consumption. One of the primary challenges for the practical use of EEG-based monitoring is to achieve a satisfactory level of accuracy in EEG classification. Connectivity between different brain regions is an important property for the classification of EEG. However, how to define the connectivity structure for a given task is still an open problem, because there is no ground truth about how the connectivity structure should be in order to maximize the classification performance. In this paper, we propose an end-to-end neural network model for EEG-based emotional video classification, which can extract an appropriate multi-layer graph structure and signal features directly from a set of raw EEG signals and perform classification using them. Experimental results demonstrate that our method yields improved performance in comparison to the existing approaches where manually defined connectivity structures and signal features are used. Furthermore, we show that the graph structure extraction process is reliable in terms of consistency, and the learned graph structures make much sense in the viewpoint of emotional perception occurring in the brain.