# Consumer-friendly EEG-based Emotion Recognition System: A Multi-scale Convolutional Neural Network Approach

arXiv: https://arxiv.org/abs/2506.16448 · HTML: https://mlsanity.com/paper/2506.16448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 76 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [End-to-End Multimodal Emotion Recognition using Deep Neural Networks](https://mlsanity.com/paper/1704.08619/) (2017)
- [TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition](https://mlsanity.com/paper/2104.02935/) (2021)

## Abstract (arXiv)

EEG is a non-invasive, safe, and low-risk method to record electrophysiological signals inside the brain. Especially with recent technology developments like dry electrodes, consumer-grade EEG devices, and rapid advances in machine learning, EEG is commonly used as a resource for automatic emotion recognition. With the aim to develop a deep learning model that can perform EEG-based emotion recognition in a real-life context, we propose a novel approach to utilize multi-scale convolutional neural networks to accomplish such tasks. By implementing feature extraction kernels with many ratio coefficients as well as a new type of kernel that learns key information from four separate areas of the brain, our model consistently outperforms the state-of-the-art TSception model in predicting valence, arousal, and dominance scores across many performance evaluation metrics.