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 language | English |
---|---|
Title of host publication | 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks and Workshops, WOWMOM 2009 |
DOIs | |
Publication status | Published - 2009 |
Event | 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks and Workshops, WOWMOM 2009 - Kos, Greece Duration: Jun 15 2009 → Jun 19 2009 |
Other
Other | 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks and Workshops, WOWMOM 2009 |
---|---|
Country/Territory | Greece |
City | Kos |
Period | 6/15/09 → 6/19/09 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Software