EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

The objective of this experiment was to determine the best possible input EEG feature for clas- sification of the workload while designing load balanc - ing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjec- tive 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 identifi - cation 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.

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