Cross Subject Mental Work Load Classification from Electroencephalographic Signals with Automatic Artifact Rejection and Muscle Pruning

Sajeev Kunjan, T. Lewis, T. Grummett, D. Powers, Kenneth Pope, S. Fitzgibbon, J. Willoughby

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    Abstract

    Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.

    Original languageEnglish
    Title of host publicationBrain Informatics and Health
    EditorsHesham Ali, Yong Shi, Giorgio A. Ascoli, Deepak Khazanchi, Michael Hawrylycz
    PublisherSpringer International Publishing
    Pages295-303
    Number of pages9
    ISBN (Print)9783319471020
    DOIs
    Publication statusPublished - 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9919 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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