Comparison of two different classifiers for mental tasks-based brain-computer interface: MLP Neural Networks vs. Fuzzy logic

Giovanni Saggio, Pietro Cavallo, Alessio Ferretti, Francesco Garzoli, Lucia Rita Quitadamo, Maria Grazia Marciani, Franco Giannini, Luigi Bianchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study is devoted to the classification of four-class mental tasks data for a Brain-Computer Interface protocol. In such view we adopted Multi Layer Perceptron 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. Five 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 of mental tasks in Brain Computer Interface protocols.

Original languageEnglish
Title of host publication2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks and Workshops, WOWMOM 2009
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks and Workshops, WOWMOM 2009 - Kos, Greece
Duration: Jun 15 2009Jun 19 2009

Other

Other2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks and Workshops, WOWMOM 2009
CountryGreece
CityKos
Period6/15/096/19/09

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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