Continuous Emotion Detection

Abstract

1. Introduction

Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection using LSTM neural networks.

This codebase implements multimodal emotion detection combining EEG signals and facial expressions, with support for feature-level and decision-level fusion strategies.

2. Objective

Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection code

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• Multimodal Learning: Combines EEG and facial expression features
• LSTM-based Architecture: Handles variable-length time series data
• Multiple Fusion Strategies:
• EEG-only model
• Face-only model
• Feature-level fusion (FLF)
• Decision-level fusion (DLF)
• 10-Fold Cross-Validation: Robust evaluation with statistical measures
• Modern TensorFlow 2.x: Updated for compatibility with latest deep learning frameworks
• Modular Design: Clean, maintainable code structure following Python best practices

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/soheilrayatdoost/ContinuousEmotionDetection.git
cd ContinuousEmotionDetection

1. Clone the repository:
2. Install dependencies:

git clone https://github.com/soheilrayatdoost/ContinuousEmotionDetection.git
cd ContinuousEmotionDetection

pip install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by soheilrayatdoost and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.