Classification of wrist movements using EEG-based wavelets features

    Research output: Book/ReportBook

    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
    Place of PublicationShanghai, China
    PublisherIEEE
    ISBN (Print)0-7803-8741-4
    DOIs
    Publication statusPublished - Sep 2005

    Publication series

    Name27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005
    PublisherIEEE

    Fingerprint

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

    Keywords

    • biomechanics
    • electroencephalography
    • feature extraction
    • handicapped aids
    • medical signal processing
    • principal component analysis
    • signal classification
    • spatiotemporal phenomena
    • wavelet transforms

    Cite this

    Lakany, H., & Conway, B. A. (2005). Classification of wrist movements using EEG-based wavelets features. (27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005). Shanghai, China: IEEE. https://doi.org/10.1109/IEMBS.2005.1615704
    Lakany, H. ; Conway, B.A. / Classification of wrist movements using EEG-based wavelets features. Shanghai, China : IEEE, 2005. (27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005).
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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.",
    keywords = "biomechanics, electroencephalography, feature extraction, handicapped aids, medical signal processing, principal component analysis, signal classification, spatiotemporal phenomena, wavelet transforms",
    author = "H. Lakany and B.A. Conway",
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    Lakany, H & Conway, BA 2005, Classification of wrist movements using EEG-based wavelets features. 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005, IEEE, Shanghai, China. https://doi.org/10.1109/IEMBS.2005.1615704

    Classification of wrist movements using EEG-based wavelets features. / Lakany, H.; Conway, B.A.

    Shanghai, China : IEEE, 2005. (27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005).

    Research output: Book/ReportBook

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    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.

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    Lakany H, Conway BA. Classification of wrist movements using EEG-based wavelets features. Shanghai, China: IEEE, 2005. (27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005). https://doi.org/10.1109/IEMBS.2005.1615704