Comparison of different classifiers on a reduced set of features for mental tasks-based brain computer interface

Giovanni Saggio, Pietro Cavallo, Giovanni Costantini, Gianluca Susi, Lucia Rita Quitadamo, Maria Grazia Marciani, Luigi Bianchi

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

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.

Original languageEnglish
Title of host publicationBIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing, Proceedings
Pages174-179
Number of pages6
Publication statusPublished - 2010
Event3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010 - Valencia, Spain
Duration: Jan 20 2010Jan 23 2010

Other

Other3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010
CountrySpain
CityValencia
Period1/20/101/23/10

Keywords

  • BCI
  • Fuzzy logic
  • Neural networks
  • SVM

ASJC Scopus subject areas

  • Signal Processing

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