Phase Synchronization for the Recognition of Mental Tasks in a Brain-Computer Interface

View/ Open
Author(s)
Gysels, Elly
Celka, Patrick
Griffith University Author(s)
Year published
2004
Metadata
Show full item recordAbstract
Brain-computer interfaces (BCIs) may be a future communication channel for motor-disabled people. In surface electroencephalogram (EEG)-based BCIs, the extracted features are often derived from spectral estimates and autoregressive models. We examined the usefulness of synchronization between EEG signals for classifying mental tasks. To this end, we investigated the performance of features derived from the phase locking value (PLV) and from the spectral coherence and compared them to the classification rates resulting from the power densities in /spl alpha/, /spl beta//sub 1/, /spl beta//sub 2/, and 8-30-Hz frequency bands. ...
View more >Brain-computer interfaces (BCIs) may be a future communication channel for motor-disabled people. In surface electroencephalogram (EEG)-based BCIs, the extracted features are often derived from spectral estimates and autoregressive models. We examined the usefulness of synchronization between EEG signals for classifying mental tasks. To this end, we investigated the performance of features derived from the phase locking value (PLV) and from the spectral coherence and compared them to the classification rates resulting from the power densities in /spl alpha/, /spl beta//sub 1/, /spl beta//sub 2/, and 8-30-Hz frequency bands. Five recordings of 60 min, acquired from three subjects while performing three different mental tasks, were analyzed offline. No artifacts were removed or rejected. We noticed significant differences between PLV and mean spectral coherence. For sole use of synchronization measures, classification accuracies up to 62% were achieved. In general, the best result was obtained combining phase synchronization measures with /spl alpha/ power spectral density estimates. The results demonstrate that phase synchronization provides relevant information for the classification of spontaneous EEG during mental tasks.
View less >
View more >Brain-computer interfaces (BCIs) may be a future communication channel for motor-disabled people. In surface electroencephalogram (EEG)-based BCIs, the extracted features are often derived from spectral estimates and autoregressive models. We examined the usefulness of synchronization between EEG signals for classifying mental tasks. To this end, we investigated the performance of features derived from the phase locking value (PLV) and from the spectral coherence and compared them to the classification rates resulting from the power densities in /spl alpha/, /spl beta//sub 1/, /spl beta//sub 2/, and 8-30-Hz frequency bands. Five recordings of 60 min, acquired from three subjects while performing three different mental tasks, were analyzed offline. No artifacts were removed or rejected. We noticed significant differences between PLV and mean spectral coherence. For sole use of synchronization measures, classification accuracies up to 62% were achieved. In general, the best result was obtained combining phase synchronization measures with /spl alpha/ power spectral density estimates. The results demonstrate that phase synchronization provides relevant information for the classification of spontaneous EEG during mental tasks.
View less >
Journal Title
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume
12
Issue
4
Copyright Statement
© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Subject
Biomedical Engineering
Electrical and Electronic Engineering