EEG Classification Using Fuzzy C-Means with Support Vector Machine Comparison

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Resource Overview

This project implements EEG classification with fuzzy C-means clustering and compares results against support vector machines using three distinct parameter optimization techniques.

Detailed Documentation

In this study, we implemented EEG signal classification using the fuzzy C-means clustering algorithm and performed comparative analysis with support vector machine approaches. To enhance SVM performance, we employed three different parameter optimization methods including grid search, Bayesian optimization, and genetic algorithms. The fuzzy C-means implementation involved calculating cluster centers through iterative membership updates using distance metrics, while SVM optimization focused on kernel parameter tuning and penalty factor selection. These methodological improvements enabled more accurate classification and analysis of EEG data patterns, with implementation featuring MATLAB's fcm function for clustering and libsvm toolbox with custom optimization wrappers for parameter tuning.

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