James Christensen (Committee Member), Ping He (Advisor), Yan Liu (Committee Member)
Master of Science in Engineering (MSEgr)
To implement adaptive aiding in modern aviation systems there is a need for accurate and reliable classification of cognitive workload. Using electroencephalogram (EEG)-derived features, it has been reported that an Artificial Neural Network (ANN) can achieve 95% or higher classification accuracy on the same day for an individual operator, but only 70% or less on a different day. To gain a further insight into this discrepancy, data from a previous study was utilized to study the classification variability. The EEG-derived features were first calculated by spectral power estimation. The variability was then analyzed by performing cognitive workload classification in which different methods of training and testing were used and different classifiers were implemented to compare classification accuracies. The classifiers include an ANN, Adaboost Algorithm, and a t-test method. The results show that when the ANN or Adaboost method is used, the amount of overlapping among training and testing data impacts the classification accuracy significantly. When there is no overlap, all classifiers can only achieve an accuracy of about 70%, with the Adaboost outperforming other classifiers slightly. By allowing some overlap, the accuracy of the ANN or Adaboost method increases significantly. It was concluded that the main source of the classification variability is the inherent variability of the EEG-derived features.
Department or Program
Department of Biomedical, Industrial & Human Factors Engineering
Year Degree Awarded
Copyright 2011, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.