# Emotional EEG Classification using Upscaled Connectivity Matrices

arXiv: https://arxiv.org/abs/2502.07843 · HTML: https://mlsanity.com/paper/2502.07843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EEG-based video identification using graph signal modeling and graph convolutional neural network](https://mlsanity.com/paper/1809.04229/) (2018)
- [Implicit Analysis of Perceptual Multimedia Experience Based on Physiological Response: A Review](https://mlsanity.com/paper/1809.04254/) (2018)
- [Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks](https://mlsanity.com/paper/2101.07069/) (2021)
- [EEG-based Emotional Video Classification via Learning Connectivity Structure](https://mlsanity.com/paper/1905.11678/) (2019)

## Abstract (arXiv)

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.