EEG classification based on movement direction and displacement

H. Lakany, Ponpisut Worrajiran, Gopal Valsan, B.A. Conway

    Research output: Contribution to conferencePaper

    4 Citations (Scopus)


    Our aim is to assess and evaluate signal processing and classification methods for extracting features from EEG signals that are useful in developing brain-computer interfaces. In this paper, we report on results of developing a method to classify wrist movements using EEG signals recorded from a subject whilst controlling a joystick and moving it in different directions. Such method could be potentially useful in building brain-computer interfaces (BCIs) where a paralysed person could communicate with a wheelchair and steer it to the desired direction using only EEG signals. Our method is based on extracting salient spatio-temporal features from the EEG signals using continuous wavelet transform. We perform principal component analysis on these features as means to assess their usefulness for classification and to reduce the dimensionality of the problem. We use the results from the PCA as means to represent the different directions. We use a simple technique based on Euclidean distance to classify the data. The classification results show that we are able to discriminate between different directions using the selected features

    Original languageEnglish
    Number of pages3
    Publication statusPublished - 2005
    Event27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Shanghai, China
    Duration: 1 Sep 20054 Sep 2005


    Conference27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Abbreviated titleIEEE-EMBS 2005


    • eeg classification
    • movement
    • displacement
    • direction


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