Understanding intention of movement from electroencephalograms

H. Lakany, B.A. Conway

    Research output: Contribution to journalArticle

    17 Citations (Scopus)

    Abstract

    In this paper, we propose a new framework for understanding intention of movement that can be used in developing non-invasive brain-computer interfaces. The proposed method is based on extracting salient features from brain signals recorded whilst the subject is actually (or imagining) performing a wrist movement in different directions. Our method focuses on analysing the brain signals at the time preceding wrist movement, i.e. while the subject is preparing (or intending) to perform the movement. Feature selection and classification of the direction is done using a wrapper method based on support vector machines (SVMs). The classification results show that we are able to discriminate the directions using features extracted from brain signals prior to movement. We then extract rules from the SVM classifiers to compare the features extracted for real and imaginary movements in an attempt to understand the mechanisms of intention of movement. Our new approach could be potentially useful in building brain-computer interfaces where a paralysed person could communicate with a wheelchair and steer it to the desired direction using a rule-based knowledge system based on understanding of the subject's intention to move through his/her brain signals.
    LanguageEnglish
    Pages295-304
    Number of pages9
    JournalExpert Systems
    Volume24
    Issue number5
    DOIs
    Publication statusPublished - 2007

    Fingerprint

    Electroencephalography
    Brain
    Brain computer interface
    Knowledge based systems
    Support vector machines
    Knowledge-based Systems
    Wheelchairs
    Support Vector Machine
    Feature extraction
    Classifiers
    Wrapper
    Electroencephalogram
    Movement
    Feature Selection
    Person
    Classifier

    Keywords

    • intention understanding
    • EEG
    • brain–computer interfaces
    • support vector machines
    • rule extraction
    • electroencephalograms

    Cite this

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    title = "Understanding intention of movement from electroencephalograms",
    abstract = "In this paper, we propose a new framework for understanding intention of movement that can be used in developing non-invasive brain-computer interfaces. The proposed method is based on extracting salient features from brain signals recorded whilst the subject is actually (or imagining) performing a wrist movement in different directions. Our method focuses on analysing the brain signals at the time preceding wrist movement, i.e. while the subject is preparing (or intending) to perform the movement. Feature selection and classification of the direction is done using a wrapper method based on support vector machines (SVMs). The classification results show that we are able to discriminate the directions using features extracted from brain signals prior to movement. We then extract rules from the SVM classifiers to compare the features extracted for real and imaginary movements in an attempt to understand the mechanisms of intention of movement. Our new approach could be potentially useful in building brain-computer interfaces where a paralysed person could communicate with a wheelchair and steer it to the desired direction using a rule-based knowledge system based on understanding of the subject's intention to move through his/her brain signals.",
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    Understanding intention of movement from electroencephalograms. / Lakany, H.; Conway, B.A.

    In: Expert Systems, Vol. 24, No. 5, 2007, p. 295-304.

    Research output: Contribution to journalArticle

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    T1 - Understanding intention of movement from electroencephalograms

    AU - Lakany, H.

    AU - Conway, B.A.

    PY - 2007

    Y1 - 2007

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    AB - In this paper, we propose a new framework for understanding intention of movement that can be used in developing non-invasive brain-computer interfaces. The proposed method is based on extracting salient features from brain signals recorded whilst the subject is actually (or imagining) performing a wrist movement in different directions. Our method focuses on analysing the brain signals at the time preceding wrist movement, i.e. while the subject is preparing (or intending) to perform the movement. Feature selection and classification of the direction is done using a wrapper method based on support vector machines (SVMs). The classification results show that we are able to discriminate the directions using features extracted from brain signals prior to movement. We then extract rules from the SVM classifiers to compare the features extracted for real and imaginary movements in an attempt to understand the mechanisms of intention of movement. Our new approach could be potentially useful in building brain-computer interfaces where a paralysed person could communicate with a wheelchair and steer it to the desired direction using a rule-based knowledge system based on understanding of the subject's intention to move through his/her brain signals.

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    KW - EEG

    KW - brain–computer interfaces

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    KW - rule extraction

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