@article{6dfa234537ef4cf2988f5611b68a3a0f,
title = "Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation",
abstract = "This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. Electroencephalogram (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 with the existing work, we show that the proposed solution is more accurate and computationally less demanding.",
keywords = "Channel selection, EEG, cognitive workload, stepwise regression",
author = "Rwan Mahmoud and Tamer Shanableh and Bodala, {Indu Prasad} and Thakor, {Nitish V.} and Hasan Al-Nashash",
note = "Funding Information: Manuscript received May 20, 2017; revised July 8, 2017; accepted July 9, 2017. Date of publication July 17, 2017; date of current version October 11, 2017. This work was supported in part by the National University of Singapore through the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under Grant R-719-001-102-232, in part by the Ministry of Education of Singapore under Grant MOE2014-T2-1-115, and in part by NGS(IB) and SINAPSE, National University of Singapore. The associate editor coordinating the review of this paper and approving it for publication was Prof. Aime Lay-Ekuakille. (Corresponding author: Hasan Al-Nashash.) R. Mahmoud and T. Shanableh are with the Department of Computer Science and Engineering, American University of Sharjah, Sharjah, UAE (e-mail: g00060845@aus.edu; tshanableh@aus.edu). Publisher Copyright: {\textcopyright} 2001-2012 IEEE.",
year = "2017",
month = nov,
day = "1",
doi = "10.1109/JSEN.2017.2727539",
language = "English (US)",
volume = "17",
pages = "7019--7028",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "21",
}