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

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    27050.pdf (468.8Kb)
    Author(s)
    Gysels, Elly
    Celka, Patrick
    Griffith University Author(s)
    Celka, Patrick
    Year published
    2004
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    Abstract
    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. ...
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    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.
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    Journal Title
    IEEE Transactions on Neural Systems and Rehabilitation Engineering
    Volume
    12
    Issue
    4
    DOI
    https://doi.org/10.1109/TNSRE.2004.838443
    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
    Publication URI
    http://hdl.handle.net/10072/5157
    Collection
    • Journal articles

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