BOARD #110: WIP: A Reconfigurable Testbed for Assessing Cognitive Workload in N-back and Multi-Object Tracking Tasks
- Conference
- Location
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Montreal, Quebec, Canada
- Publication Date
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June 22, 2025
- Start Date
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June 22, 2025
- End Date
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August 15, 2025
- Conference Session
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Computing and Information Technology Division (CIT) Poster Session
- Tagged Division
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Computing and Information Technology Division (CIT)
- Page Count
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11
- DOI
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10.18260/1-2--55927
- Permanent URL
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https://peer.asee.org/55927
- Download Count
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3
Paper Authors
Yug Patel Missouri University of Science and Technology
Yug Patel is an undergraduate student in Computer Science at the Missouri University of Science and Technology (MST). Yug has conducted research in both the Department of Computer Science and in the Department of Biology at MST, exploring the intersection of these fields through interdisciplinary projects. As a previous NSF-REU intern, Yug has gained valuable research experience and a deeper understanding of the applications of computer science in biological research. This paper presents Yug's work on a novel reconfigurable testbed for cognitive workload assessment and management, which demonstrates a comprehensive and customizable platform for evaluating cognitive workload and physiological responses under controlled experimental conditions.
Sanjana Shangle University of Texas at Dallas
Sanjana Shangle is currently pursuing a Bachelor of Science in Computer Science at the University of Texas at Dallas (UTD). Sanjana is passionate about machine learning and artificial intelligence, having applied her skills in real-time data processing, neural networks, and wearable technology integration during her NSF REU internship at Missouri University of Science and Technology. She is pursuing her work as an Undergraduate Research Assistant.
Her academic excellence is demonstrated by the prestigious Academic Excellence Scholarship she received at UTD, recognizing her outstanding performance in high school. With a focus on innovation, Sanjana seeks to leverage her skills to solve complex problems and is actively exploring opportunities in computer science and related fields.
Asir Abrar
Missouri University of Science and Technology
orcid.org/0009-0003-6729-9342
Asir Abrar is a PhD student in Computer Science at the Missouri University of Science and Technology. He earned his master's degree in Computer Science from Lamar University in Texas, USA, and completed his bachelor's degree in Computer Science and Engineering at BRAC University in Dhaka, Bangladesh. Currently, his research focus is cognitive workload assessment. He also has interests in health informatics and natural language processing.
Venkata Sriram Siddhardh Nadendla Missouri University of Science and Technology
Dr. Venkata Sriram Siddhardh Nadendla is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology. In Fall 2018, Venkata Sriram Siddhardh Nadendla worked as a postdoctoral research associate in Coordinated Science Laboratory at University of Illinois at Urbana-Champaign since Oct 2016. He received his PhD degree in Electrical and Computer Engineering from Syracuse University in 2016, his MS degree in Electrical Engineering from Louisiana State University in 2009, and his BE degree in Electronics and Computer Engineering in 2007 from SCSVMV University (India). He also worked as a research intern at ANDRO Computational Solutions, LLC, Rome, NY in the summers of 2013 and 2014. He received multiple best paper awards as well as grants from multiple funding agencies including National Science Foundation, Army Research Office, National Institute for Occupational Safety and Health and Boeing Inc. His research interests broadly span the field of computational neuroscience, machine learning, game-theory and inference networks.
K Krishnamurthy
Missouri University of Science and Technology
orcid.org/0000-0002-0219-6153
Dr. K. Krishnamurthy received his B.E. degree in Mechanical Engineering from Bangalore University, India, and his M.S. and Ph.D. degrees also in Mechanical Engineering from Washington State University, Pullman, Washington. He is currently a Professor of Mechanical Engineering in the Department of Mechanical and Aerospace Engineering at Missouri University of Science and Technology.
Abstract
Cognitive workload assessment and management are critical in managing work efficiency in high-stress environments and long-duration tasks, such as critical infrastructure operations, first-responder responses, healthcare, military, and transportation. A major challenge in developing cognitive assessment algorithms lies in designing an experimental testbed that integrates diverse systems like brain-computer interfaces, physiological sensors, and task-specific hardware for synchronized multi-modal data collection. This paper presents a novel reconfigurable testbed for assessing cognitive workload using Letter N-back, Flanker N-back, and multiple object tracking (MOT) tasks. The testbed features customizable parameters such as trial length, difficulty level, and task complexity, allowing simulation of various stress levels. The integration of Neuroelectrics EEG headsets and Bluetooth-enabled physiological sensors ensures real-time multimodal data acquisition. In addition, the modular design supports future expansion for new tasks and devices, fostering advancements in cognitive neuroscience and human performance research.
Patel, Y., & Shangle, S., & Abrar, A., & Nadendla, V. S. S., & Krishnamurthy, K. (2025, June), BOARD #110: WIP: A Reconfigurable Testbed for Assessing Cognitive Workload in N-back and Multi-Object Tracking Tasks Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--55927
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