EEG Based Cognitive State Analysis During Music Listening for Relaxation

This project analyzes human cognitive states using EEG signals while listening to different types of music.
Machine Learning and Deep Learning models are used to classify relaxation-related brain states.

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Dataset link

https://drive.google.com/file/d/1nVckQGYF5HqQsnc39vJtyux4iYIButVE/view?usp=sharing

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Description

Music has a strong influence on human emotions and mental states.
This project uses real-time EEG signals to identify cognitive states such as Relaxed, Neutral, and Normal while listening to music.

The EEG signals are:

• Filtered using Bandpass Butterworth Filter
• Processed to extract Power & Statistical Features
• Classified using ML and DL models

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Objectives

• Collect real EEG signals during music listening
• Extract meaningful brainwave features
• Classify cognitive states using ML & DL models
• Compare model performance using accuracy and confusion matrix

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Cognitive States Considered

• Alpha Music (Relaxation)
• Flute Music (Deep Calmness)
• Normal State (No Music)

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System Architecture

1. EEG Data Collection
2. Signal Preprocessing (Bandpass Filter)
3. Feature Extraction
4. Classification (ML/DL Models)
5. Performance Evaluation

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Technologies Used

Programming & Frameworks

• Python
• React.js
• Flask / FastAPI

Libraries

• NumPy
• Pandas
• SciPy
• Scikit-learn
• XGBoost
• TensorFlow / Keras
• Matplotlib

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Models Implemented

• Random Forest Classifier (RFC)
• XGBoost
• LSTM (Long Short-Term Memory)

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Features Extracted

Power Features

• Delta Power
• Theta Power
• Alpha Power
• Beta Power
• Gamma Power

Statistical Features

• Mean
• Variance
• Standard Deviation
• Skewness
• Kurtosis
• Entropy

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Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix

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How to Run the Project

Backend (Python)

pip install -r requirements.txt
python app.py

Frontend (React)

npm install
npm start

Run the app

python app.py

Open http://127.0.0.1:5000/predict

Project Structure

EEG-Cognitive-State-Analysis/
│
├── backend/
│   ├── preprocessing.py
│   ├── feature_extraction.py
│   ├── models/
│   ├── app.py
│
├── frontend/
│   ├── src/
│   ├── components/
│   └── App.jsx
│
├── dataset/
├── results/
├── README.md
└── requirements.txt