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Three-Way Emotion Classification of EEG-based Signals using Machine Learning

Ashna Purwar, Gaurav Simkar, Madhumita, Sachin Kadam

TL;DR

This work tackles three-way emotion classification from EEG signals using supervised ML on a small Muse-headband dataset. By applying a consistent preprocessing pipeline, it compares logistic regression, SVM with an RBF kernel, and random forest, reporting accuracy and F1-scores to assess performance and generalization. Random Forest emerges as the top performer with high accuracy (≈97.5%) and F1 (≈0.975), surpassing both the other models and some literature baselines on this data. The study integrates feature-extraction and visualization analyses (power spectral density, correlation, t-SNE) to interpret discriminative EEG features and discusses implications for real-time emotion-aware systems. It also points to future work incorporating temporal deep learning architectures and larger datasets to improve generalization and capture dynamic emotional processes.

Abstract

Electroencephalography (EEG) is a widely used technique for measuring brain activity. EEG-based signals can reveal a persons emotional state, as they directly reflect activity in different brain regions. Emotion-aware systems and EEG-based emotion recognition are a growing research area. This paper presents how machine learning (ML) models categorize a limited dataset of EEG signals into three different classes, namely Negative, Neutral, or Positive. It also presents the complete workflow, including data preprocessing and comparison of ML models. To understand which ML classification model works best for this kind of problem, we train and test the following three commonly used models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The performance of each is evaluated with respect to accuracy and F1-score. The results indicate that ML models can be effectively utilized for three-way emotion classification of EEG signals. Among the three ML models trained on the available dataset, the RF model gave the best results. Its higher accuracy and F1-score suggest that it is able to capture the emotional patterns more accurately and effectively than the other two models. The RF model also outperformed the existing state-of-the-art classification models in terms of the accuracy parameter.

Three-Way Emotion Classification of EEG-based Signals using Machine Learning

TL;DR

This work tackles three-way emotion classification from EEG signals using supervised ML on a small Muse-headband dataset. By applying a consistent preprocessing pipeline, it compares logistic regression, SVM with an RBF kernel, and random forest, reporting accuracy and F1-scores to assess performance and generalization. Random Forest emerges as the top performer with high accuracy (≈97.5%) and F1 (≈0.975), surpassing both the other models and some literature baselines on this data. The study integrates feature-extraction and visualization analyses (power spectral density, correlation, t-SNE) to interpret discriminative EEG features and discusses implications for real-time emotion-aware systems. It also points to future work incorporating temporal deep learning architectures and larger datasets to improve generalization and capture dynamic emotional processes.

Abstract

Electroencephalography (EEG) is a widely used technique for measuring brain activity. EEG-based signals can reveal a persons emotional state, as they directly reflect activity in different brain regions. Emotion-aware systems and EEG-based emotion recognition are a growing research area. This paper presents how machine learning (ML) models categorize a limited dataset of EEG signals into three different classes, namely Negative, Neutral, or Positive. It also presents the complete workflow, including data preprocessing and comparison of ML models. To understand which ML classification model works best for this kind of problem, we train and test the following three commonly used models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The performance of each is evaluated with respect to accuracy and F1-score. The results indicate that ML models can be effectively utilized for three-way emotion classification of EEG signals. Among the three ML models trained on the available dataset, the RF model gave the best results. Its higher accuracy and F1-score suggest that it is able to capture the emotional patterns more accurately and effectively than the other two models. The RF model also outperformed the existing state-of-the-art classification models in terms of the accuracy parameter.
Paper Structure (11 sections, 3 equations, 8 figures, 3 tables)

This paper contains 11 sections, 3 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background and Dataset
  4. Machine Learning Models
  5. Logistic Regression
  6. Support Vector Machine (SVM)
  7. Random Forest
  8. Model Training and Evaluation Procedure
  9. Dataset
  10. Results and Discussion
  11. Conclusions and Future Work

Figures (8)

  • Figure 1: EEG electrode positions according to the international 10-20 system standard murtazina2020ontology.
  • Figure 2: The distribution of emotions in the dataset.
  • Figure 3: This plot shows EEG time series of the cleaned data.
  • Figure 4: This plot shows the power spectral density (PSD) of the EEG signals.
  • Figure 5: This plot shows the correlation heatmap between different features.
  • ...and 3 more figures