Comparison of two different classifiers for mental tasks-based Brain-Computer Interface: MLP Neural Networks vs. Fuzzy Logic

摘要

This study is devoted to the classification of fourclassudmental tasks data for a Brain-Computer Interfaceudprotocol. In such view we adopted Multi LayerudPerceptron Neural Network (MLP) and Fuzzy C-means analysis for classifying: left and right hand movement imagination, mental subtraction operation and mental recitation of a nursery rhyme.udFive subjects participated to the experiment in two sessions recorded in distinct days. Different parameters were considered for the evaluation of the performances of the two classifiers: accuracy, that is, percentage of correct classifications, training time and size of the training dataset. The results show that even if the accuracies of the two classifiers are quite similar, the MLP classifier needs a smaller training set to reach them with respect to the Fuzzy one. This leads to the preference of MLP for the classification ofudmental tasks in Brain Computer Interface protocols.