Open AccessEEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING
Author(s) -
Sushil Chandra,
Kundan Lal Verma,
Greeshma Sharma,
Alok Prakash Mittal,
Devendra Jha
Publication year - 2015
Publication title -
international journal of cognitive research in science, engineering and education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 9
eISSN - 2334-8496
pISSN - 2334-847X
DOI - 10.23947/2334-8496-2015-3-1-35-41
Subject(s) - human multitasking , workload , electroencephalography , computer science , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , identification (biology) , artificial neural network , feature extraction , brain–computer interface , psychology , linguistics , philosophy , botany , psychiatry , cognitive psychology , biology , operating system
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.