Abstract
This paper describes a study of a new classifier based on EEG mapping analysis in order to develop a Brain-Computer Interface (BCI) through computer vision techniques. To this end, the data from three different subjects (BCI Competition Data Set V) have been studied to show proper EEG maps of the three mental tasks registered. A new classifier based on image analysis of the EEG maps has been presented as a suitable way to distinguish between the different tasks, showing in which conditions of frequency and time the images obtained for each mental task can be best classified. The classifier has been tested obtaining the success percentage of classification of each subject showing that this kind of techniques are able to classify between three mental tasks with good results.
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Úbeda, A., Iáñez, E., Azorín, J.M., Fernández, E. (2011). Analysis of EEG Mapping Images to Differentiate Mental Tasks in Brain-Computer Interfaces. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_26
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DOI: https://doi.org/10.1007/978-3-642-21344-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21343-4
Online ISBN: 978-3-642-21344-1
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