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Authors: Giovanni Saggio 1 ; Pietro Cavallo 1 ; Giovanni Costantini 1 ; Gianluca Susi 1 ; Lucia Rita Quitadamo 2 ; Maria Grazia Marciani 3 and Luigi Bianchi 2

Affiliations: 1 “Tor Vergata” University, Italy ; 2 “Tor Vergata” University; Fondazione Santa Lucia, IRCCS, Neuroelectrical Imaging and BCI Laboratory; “Tor Vergata” University, Italy ; 3 “Tor Vergata” University; Fondazione Santa Lucia, IRCCS, Neuroelectrical Imaging and BCI Laboratory, Italy

ISBN: 978-989-674-018-4

Keyword(s): BCI, Neural Networks, Fuzzy Logic, SVM.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: In this study a comparison among three different machine learning techniques for the classification of mental tasks for a Brain-Computer Interface system is presented: MLP neural network, Fuzzy C-Means Analysis and Support Vector Machine (SVM). In BCI literature, finding the best classifier is a very hard problem to solve, and it is still an open question. We considered only ten electrodes for our analysis, in order to lower the computational workload. Different parameters were analyzed for the evaluation of the performances of the classifiers: accuracy, training time and size of the training dataset. Results demonstrated how the accuracies of the three classifiers are nearly the same but the error margin of SVM on this reduced dataset is larger compared to the other two classifiers. Furthermore neural network needs a reduced number of trials for training purposes, reducing the recording session up to 8 times with respect to SVM and Fuzzy analysis. This suggests how, in the presented case, MLP neural network can be preferable for the classification of mental tasks in Brain Computer Interface systems. (More)

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Paper citation in several formats:
Saggio G.; Cavallo P.; Costantini G.; Susi G.; Rita Quitadamo L.; Grazia Marciani M.; Bianchi L. and (2010). COMPARISON OF DIFFERENT CLASSIFIERS ON A REDUCED SET OF FEATURES FOR MENTAL TASKS-BASED BRAIN COMPUTER INTERFACE.In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 174-179. DOI: 10.5220/0002696301740179

@conference{biosignals10,
author={Giovanni Saggio and Pietro Cavallo and Giovanni Costantini and Gianluca Susi and Lucia {Rita Quitadamo} and Maria {Grazia Marciani} and Luigi Bianchi},
title={COMPARISON OF DIFFERENT CLASSIFIERS ON A REDUCED SET OF FEATURES FOR MENTAL TASKS-BASED BRAIN COMPUTER INTERFACE},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={174-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002696301740179},
isbn={978-989-674-018-4},
}

TY - CONF

JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - COMPARISON OF DIFFERENT CLASSIFIERS ON A REDUCED SET OF FEATURES FOR MENTAL TASKS-BASED BRAIN COMPUTER INTERFACE
SN - 978-989-674-018-4
AU - Saggio, G.
AU - Cavallo, P.
AU - Costantini, G.
AU - Susi, G.
AU - Rita Quitadamo, L.
AU - Grazia Marciani, M.
AU - Bianchi, L.
PY - 2010
SP - 174
EP - 179
DO - 10.5220/0002696301740179

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