A self-organizing maps classifier structure for brain computer interfaces
Res. Biomed. Eng. (Online)
;
31(3): 232-240, July-Sept. 2015. graf
Article
in English
| LILACS
| ID: biblio-829436
ABSTRACT
AbstractIntroductionBrain Computer Interfaces provide an alternative communication path to severe paralyzed people and uses electrical signals related to brain activity in order to identify the user’s intention. In this paper a classifier based on a Self-Organizing Map is introduced.MethodsElectroencephalography signal is used on this work as a source for the user’s intention. This signal represents the brain activity and is processed in order to extract the frequency features presented to the classifier, which uses a Self-Organizing Map and a series of probability masks in order to identify the correct class.ResultsThe proposed structure was evaluated using a dataset of Electroencephalography with three mental tasks. The system was able to identify the different states of the users intention with an accuracy of 71.21% for a three-class problem using only 25 neurons for one of the users.ConclusionThe classifier proposed in this paper has an accuracy that is around the value of similar works in the literature, using the same data, but using a small time window for the classification, meaning the system can have a better time response for the user.
Full text:
Available
Index:
LILACS (Americas)
Language:
English
Journal:
Res. Biomed. Eng. (Online)
Journal subject:
Engenharia Biomdica
Year:
2015
Type:
Article
/
Project document
Affiliation country:
Brazil
Institution/Affiliation country:
Instituto Federal de Educação, Ciência e Tecnologia do Espírito Santo/BR
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