Mahalanobis distance-based classifiers are able to recognize EEG patterns by using few EEG electrodes

Fabio Babiloni, Luigi Bianchi, Francesco Semeraro, José Del R Millán, Josep Mouriño, Angela Cattini, Serenella Salinari, Maria Grazia Marciani, Febo Cincotti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationAnnual Reports of the Research Reactor Institute, Kyoto University
Pages651-654
Number of pages4
Volume1
Publication statusPublished - 2001
Event23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey
Duration: Oct 25 2001Oct 28 2001

Other

Other23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CountryTurkey
CityIstanbul
Period10/25/0110/28/01

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ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Mechanical Engineering

Cite this

Babiloni, F., Bianchi, L., Semeraro, F., Millán, J. D. R., Mouriño, J., Cattini, A., Salinari, S., Marciani, M. G., & Cincotti, F. (2001). Mahalanobis distance-based classifiers are able to recognize EEG patterns by using few EEG electrodes. In Annual Reports of the Research Reactor Institute, Kyoto University (Vol. 1, pp. 651-654)