# Emotion Recognition with Machine Learning Using EEG Signals

arXiv: https://arxiv.org/abs/1903.07272 · HTML: https://mlsanity.com/paper/1903.07272/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 10 of 10)

- [Heterogeneous Graph-Based Multimodal Brain Network Learning](https://mlsanity.com/paper/2110.08465/) (2021)
- [Electroencephalogram Emotion Recognition via AUC Maximization](https://mlsanity.com/paper/2408.08979/) (2024)
- [Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework](https://mlsanity.com/paper/2505.18175/) (2025)
- [EVOKE: Emotion Enabled Virtual Avatar Mapping Using Optimized Knowledge Distillation](https://mlsanity.com/paper/2401.06957/) (2024)
- [Real-time EEG-based Emotion Recognition using Discrete Wavelet Transforms on Full and Reduced Channel Signals](https://mlsanity.com/paper/2110.05635/) (2021)
- [Spectro Temporal EEG Biomarkers For Binary Emotion Classification](https://mlsanity.com/paper/2202.03271/) (2022)
- [Unconventional Cognitive Intelligent Robotic Control: Quantum Soft Computing Approach in Human Being Emotion Estimation -- QCOptKB Toolkit Application](https://mlsanity.com/paper/2307.06858/) (2023)
- [Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classification](https://mlsanity.com/paper/2511.12394/) (2025)
- [Three-Way Emotion Classification of EEG-based Signals using Machine Learning](https://mlsanity.com/paper/2602.00670/) (2026)
- [Boundary-aware Prototype-driven Adversarial Alignment for Cross-Corpus EEG Emotion Recognition](https://mlsanity.com/paper/2603.26713/) (2026)

## Abstract (arXiv)

In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals. EEG signals are decomposed into the gamma, beta, alpha and theta frequency bands using discrete wavelet transform (DWT), and spectral features are extracted from each frequency band. Principle component analysis (PCA) is applied to the extracted features by preserving the same dimensionality, as a transform, to make the features mutually uncorrelated. Support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states. The cross-validated SVM with radial basis function (RBF) kernel using extracted features of 10 EEG channels, performs with 91.3% accuracy for arousal and 91.1% accuracy for valence, both in the beta frequency band. Our approach shows better performance compared to existing algorithms applied to the "DEAP" dataset.