# Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2101.07069 · HTML: https://mlsanity.com/paper/2101.07069/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Connectivity Inference from Neural Recording Data: Challenges, Mathematical Bases and Research Directions](https://mlsanity.com/paper/1708.01888/) (2017)
- [JIDT: An information-theoretic toolkit for studying the dynamics of complex systems](https://mlsanity.com/paper/1408.3270/) (2014)
- [EEG-based video identification using graph signal modeling and graph convolutional neural network](https://mlsanity.com/paper/1809.04229/) (2018)
- [Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information](https://mlsanity.com/paper/1809.04208/) (2018)
- [Implicit Analysis of Perceptual Multimedia Experience Based on Physiological Response: A Review](https://mlsanity.com/paper/1809.04254/) (2018)

## Built on by (most cited first, 2 of 2)

- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [Emotional EEG Classification using Upscaled Connectivity Matrices](https://mlsanity.com/paper/2502.07843/) (2025)

## Abstract (arXiv)

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.