Welcome to our project repository! This work focuses on Classifying players as Beginner & Advanced based on their Logical thinking patterns, as captured through EEG (Electroencephalogram) signals.
We believe that the brain never lies — and through this project, we decode the electrical whispers of the mind to understand how experience reflects in neural activity during cognitively intense tasks.
- Shivani S. Patil
- Rushil B. Thakkar
- Vinayak C. Hosamani
Guide : Prof. Kaushik Mallibhat
This project leverages EEG signals from the prefrontal cortex to classify individuals into two groups:
- 🟢 Beginners
- 🔵 Advanced Players
The classification is based on logical thinking tasks (such as playing chess), where EEG theta power is a key feature to differentiate expertise levels.
- Analyze EEG data from participants during logical thinking activities
- Extract relevant features (focus on prefrontal cortex theta power)
- Train and evaluate ML models to classify individuals
- Study brainwave patterns to understand differences in cognitive effort
- EEG recordings from participants during chess-based logical tasks
- Signals recorded from the prefrontal cortex only
- Labels assigned based on player's chess expertise level
📁 Data is anonymized and structured in /data/ with preprocessing scripts provided in /scripts/.
We experimented with a range of classical ML models:
- Support Vector Machine (SVM)
- Random Forest
- CatBoost
- Theta power was consistently higher in advanced players
- Visualizations and confusion matrices are available in
/results/