Classifying motor imagery in presence of speech

Hayrettin Gürkök, Mannes Poel, Jakob Zwiers

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    8 Citations (Scopus)
    158 Downloads (Pure)


    In the near future, brain-computer interface (BCI) applications for non-disabled users will require multimodal interaction and tolerance to dynamic environment. However, this conflicts with the highly sensitive recording techniques used for BCIs, such as electroencephalography (EEG). Advanced machine learning and signal processing techniques are required to decorrelate desired brain signals from the rest. This paper proposes a signal processing pipeline and two classification methods suitable for multiclass EEG analysis. The methods were tested in an experiment on separating left/right hand imagery in presence/absence of speech. The analyses showed that the presence of speech during motor imagery did not affect the classification accuracy significantly and regardless of the presence of speech, the proposed methods were able to separate left and right hand imagery with an accuracy of 60%. The best overall accuracy achieved for the 5-class separation of all the tasks was 47% and both proposed methods performed equally well. In addition, the analysis of event-related spectral power changes revealed characteristics related to motor imagery and speech.
    Original languageUndefined
    Title of host publicationThe 2010 International Joint Conference on Neural Networks (IJCNN)
    Place of PublicationUSA
    Number of pages8
    ISBN (Print)978-1-4244-6916-1
    Publication statusPublished - 14 Oct 2010
    Event2010 IEEE International Joint Conference on Neural Networks, IJCNN 2010 - CCIB - Centre Convencions, Barcelona, Spain
    Duration: 18 Jul 201023 Jul 2010

    Publication series



    Conference2010 IEEE International Joint Conference on Neural Networks, IJCNN 2010
    Abbreviated titleIJCNN


    • METIS-271125
    • EWI-18787
    • IR-74665

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