Emotional EEG Classification using Upscaled Connectivity Matrices

TL;DR

This paper introduces a method to improve emotional EEG classification by upscaling connectivity matrices, which enhances local pattern preservation and significantly boosts CNN performance.

Contribution

The study proposes an upscaling technique for connectivity matrices in EEG classification, addressing CNN limitations in pattern retention.

Findings

Upscaling connectivity matrices improves classification accuracy.

Enhanced local pattern preservation leads to better CNN performance.

Method is validated with significant experimental improvements.

Abstract

In recent studies of emotional EEG classification, connectivity matrices have been successfully employed as input to convolutional neural networks (CNNs), which can effectively consider inter-regional interaction patterns in EEG. However, we find that such an approach has a limitation that important patterns in connectivity matrices may be lost during the convolutional operations in CNNs. To resolve this issue, we propose and validate an idea to upscale the connectivity matrices to strengthen the local patterns. Experimental results demonstrate that this simple idea can significantly enhance the classification performance.