Engineering Commons^™

Estimating Cognitive Workload In An Interactive Virtual Reality Environment Using Eeg, Christoph Tremmel, Christain Herff, Tetsuya Sato, Krzysztof Rechowicz, Yusuke Yamani, Dean J. Krusienski

Electrical & Computer Engineering Faculty Publications

With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG …

Board 141: Engineering Identity As A Predictor Of Undergraduate Students' Persistence In Engineering, Debra A. Major, Seterra D. Burleson, Xiaoxiao Hu, Kristi J. Shryock

Psychology Faculty Publications

Improving graduation rates of students who have selected and been admitted to engineering majors is a pivotal strategy in supporting national initiatives to increase the number of engineering graduates. Research suggests that the degree to which a student is attached to or belongs to engineering as a discipline better explains persistence-related outcomes than lack of interest and ability. As a result, identity frameworks have proven useful for furthering the understanding of engineering persistence. In this paper, we examine the relationship between undergraduate students’ engineering identity and persistence as an engineering major.

As part of an ongoing NSF IUSE project, a …

A Survey Of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data, S. De Silva, S. Dayarathna, G. Ariyarathne, D. Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions …

Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …