EEG Emotion Recognition Through Deep Learning

TL;DR

This paper presents a CNN-Transformer model for EEG-based emotion recognition that achieves high accuracy with fewer electrodes, enabling affordable, at-home emotional monitoring and advancing mental health diagnostics.

Contribution

It introduces a novel deep learning architecture that accurately classifies emotions from EEG signals using only 5 electrodes, reducing hardware complexity and cost.

Findings

Achieved 91% testing accuracy on a large, diverse EEG dataset.

Demonstrated effective emotion recognition with only 5 EEG electrodes.

Outperformed traditional models like SVM, DNN, and Logistic Regression.

Abstract

An advanced emotion classification model was developed using a CNN-Transformer architecture for emotion recognition from EEG brain wave signals, effectively distinguishing among three emotional states, positive, neutral and negative. The model achieved a testing accuracy of 91%, outperforming traditional models such as SVM, DNN, and Logistic Regression. Training was conducted on a custom dataset created by merging data from SEED, SEED-FRA, and SEED-GER repositories, comprising 1,455 samples with EEG recordings labeled according to emotional states. The combined dataset represents one of the largest and most culturally diverse collections available. Additionally, the model allows for the reduction of the requirements of the EEG apparatus, by leveraging only 5 electrodes of the 62. This reduction demonstrates the feasibility of deploying a more affordable consumer-grade EEG headset, thereby enabling accessible, at-home use, while also requiring less computational power. This advancement sets the groundwork for future exploration into mood changes induced by media content consumption, an area that remains underresearched. Integration into medical, wellness, and home-health platforms could enable continuous, passive emotional monitoring, particularly beneficial in clinical or caregiving settings where traditional behavioral cues, such as facial expressions or vocal tone, are diminished, restricted, or difficult to interpret, thus potentially transforming mental health diagnostics and interventions...