Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks

TL;DR

This paper presents a novel EEG emotion classification method that leverages brain connectivity features and CNNs, improving the understanding of functional brain networks in emotional state recognition.

Contribution

It introduces a new connectivity-based EEG classification system with two data-driven methods for constructing connectivity matrices, enhancing emotional state recognition accuracy.

Findings

Connectivity features improve classification performance.

Brain connectivity concentration correlates with emotional perception.

Proposed methods outperform raw data approaches.

Abstract

Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-dimensional raw data. However, this approach makes it difficult to exploit the brain connectivity information that can be effective in describing the functional brain network and estimating the perceptual state of the user. We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification by using three different types of connectivity measures. Furthermore, two data-driven methods to construct the connectivity matrix are proposed to maximize classification performance. Further analysis reveals that the level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.