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Neuroergonomics in Human-Machine-Interaction

Module name (EN):
Name of module in study programme. It should be precise and clear.
Neuroergonomics in Human-Machine-Interaction
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Neural Engineering, Master, SO 01.10.2025
Module code: NE2214.NER
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P213-0205
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
1VU+3PA (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 2
Mandatory course: no
Language of instruction:
English
Assessment:
Project work

[updated 17.06.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

NE2214.NER (P213-0205) Neural Engineering, Master, ASPO 01.04.2020 , optional course
NE2214.NER (P213-0205) Neural Engineering, Master, SO 01.10.2025 , semester 2, optional course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Dr. Farah Corona-Strauss
Lecturer:
Dozierende der Vertiefungsrichtung


[updated 17.06.2026]
Learning outcomes:
Upon successful completion of this course, students will be able to:
• Process and interpret multimodal physiological and behavioural data acquired in dynamic, ecologically valid settings (e.g., simulated operational environments, manual tasks, and interactive scenarios).
• Assess user mental states, such as cognitive workload, stress, fatigue, and affective responses, during continuous human-machine interactions.
• Evaluate user performance and state in complex work environments, applying foundational theories of neuroergonomics and engineering psychology.
• Design and develop experimental paradigms and methodological approaches to elicit, isolate, and measure targeted cognitive and affective states.


[updated 17.06.2026]
Module content:
1. Introduction to neuroergonomics
1.1 Core principles
1.2 The transitions from traditional ergonomics to neuroergonomics
1.3 Ecological validity in human-machine research
 
2. Neuroergonomic assessment methods
2.1 Measures of central nervous system activity
2.2 Measures of autonomic and peripheral nervous system activity
2.3 Wearable and contactless measures
 
3. Assessing user state in human-machine interactions (change from operator to xxx to be more general)
3.1 Cognitive effort, and sensory strain
3.2 Sensory mismatch
 
4. Technology applications
4.1 Attention Assistance Devices
4.2 XR/VR/AR
4.3 Neurotechnology driven empathetic AI systems
 
5. Future prospects for Neuroergonomics
5.1 Emerging technologies
5.2 Future directions of neuroergonomics


[updated 17.06.2026]
Teaching methods/Media:
Lecture, Student-project

[updated 17.06.2026]
Recommended or required reading:
• Parasuraman, R., & Rizzo, M. (Eds.). (2006). Neuroergonomics: The brain at work. Oxford University Press.
• Wickens, C. D., Helton, W. S., Hollands, J. G., & Banbury, S. (2021). Engineering psychology and human performance. Routledge.
• Bussolan, A., Baraldo, S., Avram, O., Urcola, P., Montesano, L., Gambardella, L. M., & Valente, A. (2025). MultiPhysio-HRC: A Multimodal Physiological Signals Dataset for Industrial Human–Robot Collaboration. Robotics, 14(12), 184.
• Corona-Strauss, F. I., Vibell, J., Francis, A. L., Lehser, M., Markert, S. M., & Strauss, D. J. (2026). Reframing neuroergonomics in an evolutionary and active inference context. Frontiers in Neuroergonomics, 7, 1796721.


[updated 17.06.2026]
 
[Wed Jun 17 16:31:18 CEST 2026, CKEY=nnih, BKEY=nem2, CID=NE2214.NER, LANGUAGE=en, DATE=17.06.2026]