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DOI: 10.1055/s-0038-1634391
Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers
Publication History
Received
13 August 2001
Accepted
30 January 2002
Publication Date:
07 February 2018 (online)
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.
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