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 language | English |
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Title of host publication | BIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing, Proceedings |
Pages | 174-179 |
Number of pages | 6 |
Publication status | Published - 2010 |
Event | 3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010 - Valencia, Spain Duration: Jan 20 2010 → Jan 23 2010 |
Other
Other | 3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010 |
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Country/Territory | Spain |
City | Valencia |
Period | 1/20/10 → 1/23/10 |
Keywords
- BCI
- Fuzzy logic
- Neural networks
- SVM
ASJC Scopus subject areas
- Signal Processing