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    Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation

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    Novel Classification System Shanableh 2017.pdf (1.524Mb)
    Date
    2017-07
    Author
    Mahmoud, Rwan Adil Osman
    Shanableh, Tamer
    Bodala, Indu P.
    Thakor, Nitish V.
    Al-Nashash, Hasan
    Advisor(s)
    Unknown advisor
    Type
    Article
    Postprint
    Peer-Reviewed
    Metadata
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    Abstract
    This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. EEG signals recorded from 16 subjects were used for workload classification. The proposed solution includes preprocessing of EEG signals and feature extraction based on statistical features. This is followed by variable selection using stepwise regression and multiclass linear classification. The presented method achieved an average classification accuracy of 93.4%. The effect of EEG channel selection on the classification accuracy is also investigated. In comparison to the existing work, we show that the proposed solution is more accurate and computationally less demanding.
    DSpace URI
    http://hdl.handle.net/11073/8896
    External URI
    http://doi.org/10.1109/JSEN.2017.2727539
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    • Department of Electrical Engineering

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