Electronic Theses and Dissertations





Document Type


Degree Name

Master of Science


Electrical and Computer Engr


Computer Engineering

Committee Chair

Mohammed Yeasin

Committee Member

Madhusudhanan Balasubramanian

Committee Member

Gavin M. Bidelman


Cognitive load refers to the amount of used working memory resources, which is limited in both capacity and duration. Predicting cognitive load from raw electroencephalogram (EEG) recordings remains a challenge because of the high degree of noise due to technical variations in the recording process and the multi-factorial nature of the mapping between the EEG data and cognitive load. We present parameter-optimized deep convolutional neural network (CNN) models to predict four levels of cognitive load. We use eigenspace-based bootstrap sampling and Generative Adversarial Network (GAN) to address the issue of noise and a small number of samples in EEG. We transform time-series signals into a spatial-spectral representation called Topomap, which maintains both spatial and spectral information embedded in EEG recordings. We use two different EEG data representations for cognitive load prediction. First, we use power spectral densities of three individual frequency bands (Theta, Alpha, Beta) to create the topomap. Second, we combine all three bands to develop a composite representation. We performed empirical evaluations to determine the role of individual frequency bands in predicting cognitive load. The prediction accuracy of CNN models built using Theta, Alpha, Beta bands, and composite representation are 89%, 89%, 91%, and 92%, respectively. The results suggest that the Beta band has the most predictive power and composite representation produces higher accuracy than the individual frequency bands.


Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.