EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING
Journal Title: International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE) - Year 2015, Vol 3, Issue 1
Abstract
The objective of this experiment was to determine the best possible input EEG feature for classifcation of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifers for workload classifcation, to identify channels which could provide classifcation with the highest accuracy and for identifcation of EEG feature which could give discrimination among workload level without adding any classifers. The result had shown Engagement Index is the best feature for neural network classifcation.
Authors and Affiliations
Sushil Chandra, M-Tech, Kundan Lal Verma, MSc, Greeshma Sharma, M-Tech, Alok Mittal, Dr. , Devendra Jha, Dr.
Keywords
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- EP ID EP34355
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