ITM Web Conf.
Volume 15, 2017II International Conference of Computational Methods in Engineering Science (CMES’17)
|Number of page(s)||7|
|Section||Computational And Artificial Intelligence|
|Published online||15 December 2017|
Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP)
Poznan University of Technology, Institute of Mechanical Technology, Skłodowska-Curie Square 5, 60-965 Poznań, Poland
* Corresponding author: firstname.lastname@example.org
The paper describes the research on the classifiers for brain-computer interface (BCI) based on Steady State Visually Evoked Potential (SSVEP). Authors presented research on the checking the usability of classifiers for recognizing an EEG signal during the stimulus. Three classifiers have been checked: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and one based on Artificial Neural Network (ANN). First part is concentrated on brain-computer interfaces and classification of them. The second part describes algorithms of all using classifiers. In the next part, authors present test stand and how the experiment is built. The last part consists of results of these tests. The best was the classifier based on Artificial Neural Network – up to 95% of correct identified. The worst results were obtained from Support Vector Machine – about 70%.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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