Author:
Cincotti F.,Mattia D.,Babiloni C.,Carducci F.,Bianchi L.,Millán del R.,Mouriño J.,Salinari S.,Marciani M. G.,Babiloni F.
Abstract
Summary
Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes.
Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used.
Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes.
Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.
Subject
Health Information Management,Advanced and Specialised Nursing,Health Informatics
Cited by
18 articles.
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