Thesis
Using around-the ear electroencephalography (EEG) adhesive technology to classify mental workload (MWL)
University of Iowa
Master of Science (MS), University of Iowa
Spring 2020
DOI: 10.17077/etd.005321
Abstract
This thesis explores the feasibility of a physiological assessment method to monitor the Mental Workload (MWL) of an operator in flight-related operations. MWL is the mental effort put forth by an operator during task performance. This effort is affected by the task at hand, the skills and background of the operator, and the environment in which the task is performed. Certain levels of MWL yield optimal performance, while other levels of MWL yield lower performance. Optimizing the MWL of an operator can improve safety and performance. The MWL of an operator can only be optimized if it is accurately and reliably depicted. Current MWL assessments consist of subjective ratings, performance metrics, and physiological signals. Since MWL is an intangible construct with no “actual workload” values, each method is assessed against each other. There are pros and cons to each of the methods are discussed in this thesis.
An experiment was conducted to test the reliability of a new electroencephalogram (EEG) data collection configuration. Current EEG configurations are in the form of caps and are bulky and intrusive on the operator. The new technology, termed cEEGrids, may be able to overcome this physical barrier and have the potential to be used during in-flight operations. The experiment consisted of two tasks with three levels of difficulty. Subjective ratings, performance measures, and EEG signals were collected during the trials. The EEG signals are processed using the Fast Fourier Transform (FFT) to export the spectral power content of the signals. The subjective ratings, performance metrics, and EEG spectral powers were used to build a Linear Regression Model. The subjective rating was used as the response variable and the performance and EEG signals were used as the predictor variables.
Results showed that the performance and EEG signals were able to reliably predict the subjective rating of a trial. The model had an R2 value of 0.80 with the training data. There was a 3% error rate on the model predictions for the train data. The test trials had a Pearson Correlation Coefficient of 0.348 and an 18% error for the model predictions. These are promising results for the feasibility of performance and EEG signals from the cEEGrids to reliably predict subjective ratings. However, there are still implications for the results and future research needs to be conducted before the reasoning for using cEEGrid technology in flight is fully validated.
Details
- Title: Subtitle
- Using around-the ear electroencephalography (EEG) adhesive technology to classify mental workload (MWL)
- Creators
- Katharine Woodruff
- Contributors
- Thomas Schnell (Advisor)Priyadarshini Pennathur (Committee Member)Ruben Beltran Del Rio (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Industrial Engineering
- Date degree season
- Spring 2020
- Publisher
- University of Iowa
- DOI
- 10.17077/etd.005321
- Number of pages
- xii, 99 pages
- Copyright
- Copyright 2020 Katharine Woodruff
- Language
- English
- Description illustrations
- illustrations (some color)
- Description bibliographic
- Includes bibliographical references (pages 95-99).
- Public Abstract (ETD)
Humans have limited capacities for the amount of physical and mental labor they can incur without performance suffering. As advancements in technology reduce the physical labor necessary to perform tasks, mental demands tend to increase. Performance can suffer when the mental workload (MWL) demands of a task or too low or too high. Finding the optimal MWL of an operator can optimize performance of a task. Optimization can only occur when the state of the operator is accurately monitored. This thesis explored the usability of the cEEGrid EEG technology to classify MWL during flight-related operations. The general hypothesis was that subjective ratings can be predicted by performance and the EEG signals. To test that, a dual task experiment was designed to elicit a spectrum of MWL levels on an operator. A linear regression model was developed to predict subjective ratings based on the performance metrics and EEG signals. The model was then applied to the trial data. The model indicated acceptable predictive powers for subjective ratings. These promising results yield high expectations for the future use of cEEGrid technology to reliably and continuously predict MWL levels of an operator in flight.
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9983949695002771
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