A self-organizing maps classifier structure for brain computer interfaces

Abstract

Introduction Brain 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. Methods: Electroencephalography 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. Results: The 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. Conclusion: The 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.