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  • A fair comparison of the EEG signal classification methods for alcoholic subject identification

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    Awrangjeb459155-Accepted.pdf (4.411Mb)
    File version
    Accepted Manuscript (AM)
    Author(s)
    Awrangjeb, M
    Rodrigues, JDC
    Stantic, B
    Estivill-Castro, V
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Awrangjeb, Mohammad
    Stantic, Bela
    Year published
    2020
    Metadata
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    Abstract
    The electroencephalogram (EEG) signal, which records the electrical activity in the brain, is useful for assessing the mental state of the alcoholic subject. Since the public release of an EEG dataset by the University of California, Irvine, there have been many attempts to classify the EEG signals of alcoholic' and 'healthy' subjects. These classification methods are hard to compare as they use different subsets of the dataset and many of their algorithmic settings are unknown. The comparison of their published results using the inconsistent and unknown information is unfair. This paper attempts a fair comparison by presenting ...
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    The electroencephalogram (EEG) signal, which records the electrical activity in the brain, is useful for assessing the mental state of the alcoholic subject. Since the public release of an EEG dataset by the University of California, Irvine, there have been many attempts to classify the EEG signals of alcoholic' and 'healthy' subjects. These classification methods are hard to compare as they use different subsets of the dataset and many of their algorithmic settings are unknown. The comparison of their published results using the inconsistent and unknown information is unfair. This paper attempts a fair comparison by presenting a level playing field where a public subset of the dataset is employed with known algorithmic settings. Two recently proposed high performing EEG signal classification methods are implemented with different classifiers and cross-validation techniques. While compared it is observed that the wavelet packet decomposition method with the Naïve Bayes classifier and the k-fold cross validation technique outperforms the other method.
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    Conference Title
    2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)
    DOI
    https://doi.org/10.1109/IVCNZ51579.2020.9290683
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/404377
    Collection
    • Conference outputs

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