# EEG-based Emotional Video Classification via Learning Connectivity Structure

## TL;DR

This paper introduces an end-to-end neural network that automatically learns optimal brain connectivity structures from raw EEG data to improve emotional video classification accuracy.

## Contribution

It presents a novel neural network model that learns connectivity structures directly from raw EEG signals, eliminating the need for manual feature engineering.

## Key findings

- Improved classification accuracy over existing methods.
- Reliable and consistent graph structure extraction.
- Learned structures align with emotional perception in the brain.

## Abstract

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.