EEG-data-classification-using-Genetic-algorithms

In this repository, features were extracted from EEG data, and the best features were selected for classifying the signals using Genetic Algorithms.

phase 1:

• the features were extracted from the channels.
• using the fisher criterion, the best features were chosen.
• using 5-fold cross-validation, an MLP network was implemented to classify the data. -- * for better classification, the hyperparameters such as the number of layers, number of neurons, and activation functions were found.
• the above step was done for implementing an RBF network whose hyperparameters, such as the number of neurons and their spreads, were optimized.

phase 2:

• Feature selection was made using a Genetic Algorithm
• The precision of an MLP classifier was used as the objective function
• MLP and RBF neural networks were trained using selected features.
• Better outcomes were observed.