EEG classification based on movement direction and displacement

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

    Research output: Contribution to conference › Paper

    4 Citations (Scopus)

    Abstract

    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

    LanguageEnglish
    Pages5404-5407
    Number of pages3
    DOIs
    Publication statusPublished - 2005
    Event27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Shanghai, China
    Duration: 1 Sep 2005 → 4 Sep 2005

    Conference

    Conference27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Abbreviated titleIEEE-EMBS 2005
    CountryChina
    CityShanghai
    Period1/09/05 → 4/09/05

    Fingerprint

    Electroencephalography
    Brain computer interface
    Wheelchairs
    Principal component analysis
    Wavelet transforms
    Signal processing

    Keywords

    • eeg classification
    • movement
    • displacement
    • direction

    Cite this

    Lakany, H., Worrajiran, P., Valsan, G., & Conway, B. A. (2005). EEG classification based on movement direction and displacement. 5404-5407. Paper presented at 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China. https://doi.org/10.1109/IEMBS.2005.1615704
    Lakany, H. ; Worrajiran, Ponpisut ; Valsan, Gopal ; Conway, B.A. / EEG classification based on movement direction and displacement. Paper presented at 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China.3 p.
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    abstract = "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",
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    author = "H. Lakany and Ponpisut Worrajiran and Gopal Valsan and B.A. Conway",
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    Lakany, H, Worrajiran, P, Valsan, G & Conway, BA 2005, 'EEG classification based on movement direction and displacement' Paper presented at 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China, 1/09/05 - 4/09/05, pp. 5404-5407. https://doi.org/10.1109/IEMBS.2005.1615704

    EEG classification based on movement direction and displacement. / Lakany, H.; Worrajiran, Ponpisut; Valsan, Gopal; Conway, B.A.

    2005. 5404-5407 Paper presented at 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China.

    Research output: Contribution to conference › Paper

    TY - CONF

    T1 - EEG classification based on movement direction and displacement

    AU - Lakany, H.

    AU - Worrajiran, Ponpisut

    AU - Valsan, Gopal

    AU - Conway, B.A.

    N1 - Also presented at Neuroscience 2005 annual meeting.

    PY - 2005

    Y1 - 2005

    N2 - 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

    AB - 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

    KW - eeg classification

    KW - movement

    KW - displacement

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    Lakany H, Worrajiran P, Valsan G, Conway BA. EEG classification based on movement direction and displacement. 2005. Paper presented at 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China. https://doi.org/10.1109/IEMBS.2005.1615704

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