Final-Project

EEG Eye State Classification Using Machine Learning Techniques

The project explores machine learning models for classifying eye states (open or closed) using EEG data.

Paper(Manuscript)

The full report can be found in paper.pdf.

Presentation slides

Slides for data description

Project Structure

FinalProject/
├── paper/              # Final paper and source file
│   ├── paper.Rmd       # R Markdown source of the report
│   └── paper.pdf       # Compiled PDF of the final report
│
├── slides/             # Presentation slides
│   ├── index.Rmd       # R Markdown source of the slides
│   └── index.html      # Rendered HTML slides (e.g., xaringan or revealjs)
│
├── code/               # Scripts for data processing and modeling
├── data/               # Raw or preprocessed EEG data
├── figures/            # Visualizations and exported plots
└── README.md           # Project overview and instructions

Models Used

• Logistic Regression
• Support Vector Machine (SVM)
• Random Forest
• Multi-layer Perceptron (MLP)
• k-Nearest Neighbors (kNN)

Feature Engineering

• Oversampling (to handle imbalance)
• LASSO and Random Forest feature selection
• Domain-specific interaction terms

Results

• SVM achieved the best performance:
  • Accuracy: 94.6%
  • AUC: 0.982
• McNemar’s test showed SVM, MLP, and kNN performed similarly (p > 0.05), while logistic regression performed significantly worse (p < 0.001).

Dataset

Data: UCI EEG Eye State Dataset

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For more information, please see the full report.