Classification of EEG mental patterns by using two scalp electrodes and Mahalanobis distance-based classifiers.
OBJECTIVE
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.
Cincotti
F
F
IRCCS, Fondazione Santa Lucia, Rome, Italy.
Mattia
D
D
Babiloni
C
C
Carducci
F
F
Bianchi
L
L
del R Millán
J
J
Mouriño
J
J
Salinari
S
S
Marciani
M G
MG
Babiloni
F
F
D001698:Biomedical Engineering; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000145:classification* / Q000295:instrumentation; D006225:Hand / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008606:Mental Processes; D008959:Models, Neurological; D009068:Movement / Q000502:physiology; D012535:Scalp; D014584:User-Computer Interface