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.
Mahalanobis distance-based classifiers are able to recognize EEG patterns by using few EEG electrodes / Babiloni, Fabio; L., Bianchi; F., Semeraro; J. D., Millan; J., Mourino; A., Cattini; Salinari, Serenella; M. G., Marciani; Cincotti, Febo. - 23:(2001), pp. 651-654. (Intervento presentato al convegno 23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society tenutosi a ISTANBUL, TURKEY nel OCT 25-28, 2001).
Mahalanobis distance-based classifiers are able to recognize EEG patterns by using few EEG electrodes
BABILONI, Fabio;SALINARI, Serenella;CINCOTTI, FEBO
2001
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.