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

H. Lakany, B.A. Conway

    Research output: Book/ReportBook

    Abstract

    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.
    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
    PublisherICGST

    Fingerprint

    Electroencephalography
    Learning systems
    Brain computer interface
    Feature extraction

    Keywords

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

    Cite this

    Lakany, H., & Conway, B. A. (2005). Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach. (Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05). Cairo, Egypt.
    Lakany, H. ; Conway, B.A. / Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach. Cairo, Egypt, 2005. 4 p. (Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05).
    @book{7959ee3324a3439f95681f7d4dffd51a,
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    abstract = "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.",
    keywords = "eeg data, brain computer interfaces, wrist movements, machine learning",
    author = "H. Lakany and B.A. Conway",
    year = "2005",
    month = "12",
    language = "English",
    isbn = "21968/2005",
    series = "Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05",
    publisher = "ICGST",

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    Lakany, H & Conway, BA 2005, Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach. Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05, Cairo, Egypt.

    Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach. / Lakany, H.; Conway, B.A.

    Cairo, Egypt, 2005. 4 p. (Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05).

    Research output: Book/ReportBook

    TY - BOOK

    T1 - Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach

    AU - Lakany, H.

    AU - Conway, B.A.

    PY - 2005/12

    Y1 - 2005/12

    N2 - 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.

    AB - 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.

    KW - eeg data

    KW - brain computer interfaces

    KW - wrist movements

    KW - machine learning

    M3 - Book

    SN - 21968/2005

    T3 - Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05

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

    CY - Cairo, Egypt

    ER -

    Lakany H, Conway BA. Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach. Cairo, Egypt, 2005. 4 p. (Proceedings of the first ICGST International Conference on Artificial Intelligence and Machine Learning AIML 05).