This repository contains the code and documentation for a series of projects in the field of computational intelligence. The projects are part of a course and are divided into different phases.
In the first phase, the goal is to implement an intelligent system for detecting seizures in epileptic patients using EEG signals. A dataset of EEG signals is provided, and you are required to preprocess the data, extract features, and improve the features' performance. You will classify the signals to achieve the final goal of seizure detection. For more details on this phase, please read the Project Phase 1 README.
The second phase focuses on feature evaluation and selection. You will implement a comprehensive feature selection method, evaluating features based on their individual performance and how different they are from each other. You will also discuss the reasons behind feature selection and which features carried information while eliminating redundancies. For more details on this phase, please read the Project Phase 2 README.
The final phase brings together the knowledge gained from previous phases and combines it with the implementation of a neural network. Your task is to create a system that can differentiate seizure data from normal data in a new dataset using EEG signals. This project can be done in groups of two. It involves dataset preparation, classification using a convolutional neural network, and the presentation of results. For more details on this phase, please read the Project Final Phase README.
The project is organized into different directories for each phase, and you can find more detailed information within each phase's README.
Feel free to explore the source code and documentation in the respective project directories.
Note: The project details and specifications are part of a course and are intended for educational purposes.