Brainwave Classification Using EEG Signals — Machine Learning Model

This project focuses on building a machine learning model that analyzes EEG (Electroencephalogram) signals to classify brainwave patterns. The workflow includes data loading, signal preprocessing, visualization, and training different ML models to evaluate their performance.

Main Components:

1. Dataset

EEG signals saved in a structured format.

Each row represents a full EEG sample containing multiple time-series points.

A label column indicates the class/category of the brain activity.

2. Preprocessing

Splitting the dataset into:

Features (X): Raw EEG signal values

Labels (y): Class output

Converting signals into numerical lists for visualization.

Selecting a random EEG signal and plotting it to inspect waveform quality.

3. Exploratory Data Analysis (EDA)

Viewing data samples

Checking signal consistency

Plotting raw EEG waveforms

Understanding patterns in brain activity

4. Machine Learning Models

Several models are implemented for classification, including:

Logistic Regression

Baseline classifier for EEG binary/multi-class tasks

SVM (Support Vector Machine)

Effective for high-dimensional EEG data

Naive Bayes

Fast probabilistic model suitable for raw signal features

Each model is trained and evaluated on stratified training/testing splits.

5. Evaluation Metrics

The project computes:

Accuracy

Precision

Recall

F1-score

Classification report

Metrics help compare how each model performs when distinguishing EEG signal classes.

6. Visualization

Plotting random EEG samples

Visual inspection of noisy vs clean signals

Helps validate preprocessing and feature quality

7. Summary

The project demonstrates how EEG signals can be processed and classified using standard ML algorithms. This pipeline can be extended to:

Cognitive state detection

Brain–computer interface (BCI) applications

Medical diagnostics

Neuro-feedback systems