Martinez-Murcia, F. J. 1 , Ortiz, A. 1 , Luque, J. L. 2 , Giménez, A. 3 , López-Pérez, P. J. 2, 4 & López-Zamora, M. 2
1 Department of Communications Engineering, University of Malaga (Spain)
2 Department of Developmental and Educational Psychology, University of Malaga (Spain)
3 Department of Basic Psychology, University of Malaga (Spain)
4 European University of Atlantic
Electroencephalography (EEG) signals provide an important
source of information of brain activity at different areas. This information
can be used to diagnose brain disorders according to different activation
patterns found in controls and patients. This acquisition technology can
be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a speci c difficulty
in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalente is estimated between 5% and 12% of
the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the
spectral density at each channel. This relationship is computed by means
of different correlation measures, inferring connectivity-like markers that
are eventually selected and classi ed by a linear support vector machine.
The experiments performed shown AUC values up to 0.7, demonstrating
the applicability of the proposed approach for objective DD diagnosis.