This work proposes classification of two-class motor imagery electroencephalogram signals using different automated machine learning algorithms. Here data are decomposed into various frequency bands identified by wavelet transform and will span the range of 0–30 Hz.

    Design/methodology/approach

    Statistical measures will be applied to these frequency bands to identify features that will subsequently be used to train the classifiers. Further, the assessment parameters such as SNR, mean, SD and entropy are calculated to analyze the performance of the proposed work.

    Findings

    The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.

    Originality/value

    The experimental results show that the proposed work yields better accuracy for all classifiers when compare to state-of-the-art techniques.


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    Titel :

    Electroencephalogram (EEG) signal classification for brain–computer interface using discrete wavelet transform (DWT)


    Weitere Titelangaben:

    EEG signal classification for BCI using DWT


    Beteiligte:
    Rajashekhar, U. (Autor:in) / Neelappa, D. (Autor:in) / Rajesh, L. (Autor:in)


    Erscheinungsdatum :

    07.01.2022


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Schlagwörter :



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    Ikenishi, T. / Kamada, T. / Nagai, M. et al. | British Library Conference Proceedings | 2007


    Classification of Driver Steering Intention Based on Brain-Computer Interface Using Electroencephalogram

    Nagai, Masao / Kamada, Takayoshi / Ikenishi, Toshihito | SAE Technical Papers | 2007


    Knock signal analysis using the discrete wavelet transform

    Borg,J.M. / Saikalis,G. / Oho,S. et al. | Kraftfahrwesen | 2006


    Knock Signal Analysis Using the Discrete Wavelet Transform

    Saikalis, George / Borg, Jonathan M. / Cheok, Ka C. et al. | SAE Technical Papers | 2006