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)

Report Date:

2001-10-25

Pagination or Media Count:

5.0

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.

Subject Categories:

  • Anatomy and Physiology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE