Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach

H. Lakany, B.A. Conway

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    Our goal is to develop an algorithm for feature extraction and classification to be used in building brain-computer interfaces. In this paper, we present preliminary results for classifying EEG data of imaginary wrist movements. We have developed an algorithm based on the spatio-temporal features of the recorded EEG signals. We discuss the differences between the feature vectors selected for both actual and imaginary wrist movements and compare classification results.
    Original languageEnglish
    Place of PublicationCairo, Egypt
    Number of pages4
    Publication statusPublished - Dec 2005

    Publication series

    NameProceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05


    • eeg data
    • brain computer interfaces
    • wrist movements
    • machine learning

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