期刊名称：International Journal of Cognitive Research in Science, Engineering and Education
印刷版ISSN：2334-847X
电子版ISSN：2334-8496
出版年度：2015
期号：4339
页码：35-41
出版社：The Association for the Development of Science, Engineering and Education
摘要：The objective of this experiment was to determine the best possible input EEG feature for classification 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 Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.
