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Transfer Learning Approach in Classification of BCI Motor Imagery Signal Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Binary Classification of Cognitive Workload Levels with Oculography Features

Authors : Monika Kaczorowska, Martyna Wawrzyk, Małgorzata Plechawska-Wójcik

Published in: Computer Information Systems and Industrial Management

Publisher: Springer International Publishing

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Abstract

Assessment of cognitive workload level is important to understand human mental fatigue, especially in the case of performing intellectual tasks. The paper presents a case study on binary classification of cognitive workload levels. The dataset was received from two versions of the digit symbol substitution test (DSST), conducted on 26 healthy volunteers. A screen-based eye tracker was applied during an examination gathering oculographic data. DSST test results such as total number of matches and error ratio were also applied. Classification was performed with several different machine learning models. The best accuracy (97%) was achieved with linear SVM classifier. The final dataset for classification was based on nine features selected with the Fisher score feature selection method.

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Metadata
Title
Binary Classification of Cognitive Workload Levels with Oculography Features
Authors
Monika Kaczorowska
Martyna Wawrzyk
Małgorzata Plechawska-Wójcik
Copyright Year
2020
Book
Computer Information Systems and Industrial Management

Print ISBN: 978-3-030-47678-6

Electronic ISBN: 978-3-030-47679-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-47679-3_21

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