Paper Info
Title
Comparison of Different Classifiers on a Reduced Set of Features for Mental Tasks-based Brain Computer Interface
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.
Year
Venue
Keywords
2010
BIOSIGNALS 2010: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON BIO-INSPIRED SYSTEMS AND SIGNAL PROCESSING
BCI,Neural Networks,Fuzzy Logic,SVM
Field
DocType
Citations 
Computer science,Fuzzy logic,Support vector machine,Brain–computer interface,Speech recognition,Artificial intelligence,Artificial neural network
Conference
0
PageRank 
References 
Authors
0.34
9
7
Name
Order
Citations
PageRank
Giovanni Saggio14116.27
Pietro Cavallo211.09
G. Costantini311813.88
Gianluca Susi442.11
Lucia Rita Quitadamo5285.14
Maria Grazia Marciani6344.80
Luigi Bianchi7233.01