# Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information

arXiv: https://arxiv.org/abs/1809.04208 · HTML: https://mlsanity.com/paper/1809.04208/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition](https://mlsanity.com/paper/2208.00883/) (2022)
- [Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks](https://mlsanity.com/paper/2101.07069/) (2021)
- [EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification](https://mlsanity.com/paper/2107.03983/) (2021)
- [Automated Feature Extraction on AsMap for Emotion Classification using EEG](https://mlsanity.com/paper/2201.12055/) (2022)

## Abstract (arXiv)

Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in previous studies, which can account for synchronous activations of different brain regions. In addition, we develop a method to effectively capture asymmetric brain activity patterns that are important for emotion recognition. Experimental results confirm the effectiveness of our approach.