Brain Signal Classifier

by Tomas Cabrera.

Table of Contents

• Introduction
• Data Description
• Features
• Installation
• Usage
• Results
• Contributing
• License

Introduction

This project analyzes EEG signals to classify different brain activities. It focuses on detecting changes when a subject listens to music (Beethoven and death metal) compared to a baseline state.

Data Description

The EEG dataset includes recordings from a single subject under different conditions:

• Baseline: The subject is at rest; used as negative examples.
• Beethoven: EEG while listening to Beethoven's music.
• Deathmetal: EEG while listening to death metal music.

Data Files

The data files are located in the data directory:

• baseline.dat
• beethoven.dat
• deathmetal.dat

Each .dat file contains the following columns:

• timestamp
• counter
• eeg
• attention
• meditation
• blinking

Recording Details

• Duration: Each recording is between 10 and 11 minutes long.
• Sampling Frequency (Fs): 512 Hz.

Features

Data Loading

• Reads EEG data from .dat files using pandas.

Signal Processing

• Temporal Smoothing: Applies a moving average filter to smooth the EEG signals.
• Spectral Filtering: Uses a Butterworth bandpass filter to isolate frequencies between 1 Hz and 50 Hz.

Visualization

• Plots raw, smoothed, and filtered EEG signals for each condition.

Feature Extraction

• Computes statistical features (mean, standard deviation, skewness, kurtosis) over sliding windows.
• Labels extracted features according to the condition (Beethoven or death metal).

Classification

• Merges features into a single dataset.
• Splits data into training and testing sets.
• Trains a Random Forest classifier to distinguish between the two conditions.

Evaluation

• Generates a confusion matrix.
• Calculates accuracy, precision, recall, and F1-score.
• Provides a classification report.

Installation

Requirements

• Python +3.11

Packages:

• pandas
• numpy
• matplotlib
• scipy
• scikit-learn
• seaborn

Step 1: Clone the Repository

git clone https://github.com/tomycabre/BrainSignalClassifier.git
cd AI-Web-Scrapper

Step 2: Virtual Environment Setup

Windows

py -m venv venv

MacOS

python3 -m venv venv

Step 3: Activate the Virtual Environment

Windows

.\venv\Scripts\activate.bat

MacOS

source venv/bin/activate

Step 4: Install Required Packages

Windows

py -m pip install -r requirements.txt

MacOS

pip install -r requirements.txt

Usage

Prepare Data:

Ensure that the EEG data files (baseline.dat, beethoven.dat, deathmetal.dat) are placed in the data directory.

Run the Main Script:

python main.py

Process Overview:

• Evaluation:
  • Displays a confusion matrix.
  • Prints accuracy and classification report.

Results

Evaluation Metrics

• Accuracy: Proportion of correct predictions over total predictions.
• Precision: True positives divided by the sum of true and false positives.
• Recall: True positives divided by the sum of true positives and false negatives.
• F1-Score: Harmonic mean of precision and recall.

Sample Output

Reporte de Clasificación:
precision recall f1-score support

           0       0.99      0.99      0.99     15234
           1       0.99      0.99      0.99     14871

    accuracy                           0.99     30105

macro avg 0.99 0.99 0.99 30105
weighted avg 0.99 0.99 0.99 30105

Plots

• Raw Signals: Visual representation of the original EEG data.
• Smoothed Signals: EEG data after applying the moving average filter.
• Filtered Signals: EEG data after bandpass filtering.
• Confusion Matrix: Heatmap showing the classifier's performance.

Contributing

We welcome contributions! Please open issues or submit pull requests.

How to Contribute

1. Fork the Repository:

   • Click the 'Fork' button on the repository page.

2. Clone Your Fork:

   git clone https://github.com/tomycabre/BrainSignalClassifier.git
   cd brain-signal-classifier

3. Create a New Branch:

   git checkout -b feature/your_feature_name

4. Make Changes: Implement your feature or fix.

5. Commit Changes:

   git commit -am 'Add new feature'

6. Push to Your Fork:

   git push origin feature/your_feature_name

7. Submit a Pull Request: Go to the original repository and create a new pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.