Authors:
Maria Claudia F. Castro
;
João Pedro de O. P. Galhianne
and
Esther Luna Colombini
Affiliation:
Centro Universitário da FEI, Brazil
Keyword(s):
EEG, Band-power Extraction, Pattern Recognition, Linear Discriminant Analysis (LDA).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
C EEG channel data are usually used when building systems that aim at distinguishing among right and left hand movements. Few alternatives use multichannel systems when bigger sets of motor imagery are subject to classification and more inputs are required. In this context, this work proposes the use of 8 EEG channels (F,C,P, and O), disposed in a non-conventional set up, to classify up to 4 motor imagery of the upper limbs through a Linear Discriminant Analysis classifier. A spatial feature selection, prior to classification, is applied in order to improve the classification accuracy. For the many channel combinations tested, results suggest that, in addition to the motor areas, other brain areas should be considered. For the proposed system, the best classification accuracy was achieved when distinguishing between left arm and left hand (89.74%) and using only the electrodes in F areas. For the right versus left hand a 71.80% rate was obtained, with electrodes either in P and O are
as or in F and P areas. To discriminate between arms and hands, independently of the body side, the best score was 83.33%, for F and P channels, whereas for right and left limbs the best score was 66.02%, with only P channels. The best classification accuracy for the 4 movement problem achieved 50.00%, using all electrodes.
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