Accession Number : ADA411863


Title :   Mahalanobis Distance-Based Classifiers are Able to Recognize EEG Patterns by Using Few EEG Electrodes


Descriptive Note : Conference paper


Corporate Author : UNIVERSITA DEGLI STUDI LA SAPIENZA ROME(ITALY)


Personal Author(s) : Babiloni, Fabio ; Bianchi, Luigi ; Semeraro, Francesco ; Millan, Jose del ; Mourinyo, Josep


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a411863.pdf


Report Date : 25 Oct 2001


Pagination or Media Count : 5


Abstract : In this paper, we explore the use of quadratic classifiers based on Mahalanobis distance to detect EEG patterns from a reduced set of recording electrodes. Such classifiers used the diagonal and full covariance matrix of EEG spectral features extracted from EEG data. Such data were recorded from a group of 8 healthy subjects with 4 electrodes, placed in C3, P3, C4, P4 position of the international 10-20 system. Mahalanobis distance classifiers based on the use of full covariance matrix are able to detect EEG activity related to imagination of movement with affordable accuracy (average score 98%). Reported average recognition data were obtained by using the cross-validation of the EEG recordings for each subject. Such results open the avenue for the use of Mahalanobis-based classifiers in a brain computer interface context, in which the use of a reduced set of recording electrodes is an important issue.


Descriptors :   *ELECTROENCEPHALOGRAPHY , SYMPOSIA , ELECTRODES , CLASSIFICATION , ITALY , PATTERN RECOGNITION , SPECTRUM ANALYSIS


Subject Categories : Anatomy and Physiology


Distribution Statement : APPROVED FOR PUBLIC RELEASE