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Master’s in Cognitive Ergonomics and Neuroergonomics Engineering

Online master's programme of 1,900 hours in Cognitive Ergonomics and Neuroergonomics Engineering, with 12 modules covering applied cognition, neuroscience, EEG, fNIRS, mental workload, vigilance and fatigue, cognitive and neuroadaptive interfaces, research methods and neuroethics, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Apply models of human cognition to system design.

02

Record and process EEG and fNIRS signals in applied settings.

03

Measure mental workload using subjective, performance and physiological methods.

04

Assess vigilance, mental fatigue and attention in operations.

05

Design cognitive and neuroadaptive interfaces.

06

Develop rigorous, reproducible neuroergonomics experiments.

07

Weigh up the ethical and privacy implications of neurotechnology.

Profile

Who it's for

  • Neuroergonomics researchers.
  • Cognitive human factors engineers.
  • Adaptive interface specialists.
  • Neurophysiological data analysts.
  • Designers of decision-support systems.
  • Cognitive ergonomics consultants.
  • Engineering or science graduates looking to move into aviation and air traffic control.
Standards & tools

What you'll work with

  • ISO 10075 (ergonomics of mental workload)
  • ISO 11064 (ergonomic design of control centres)
  • ISO 9241-210 (human-centred design)
  • Regulation (EU) 2016/679 (GDPR)
  • WMA Declaration of Helsinki (ethics of research with human participants)
  • OECD Recommendation on Responsible Innovation in Neurotechnology
  • ISO 6385 (ergonomic principles in the design of work systems)
  • EEGLAB
  • MNE-Python
  • Homer3
  • PsychoPy
  • Tobii Pro Lab
Syllabus

12 modules · 1900 h

  1. 1.1Human information processing
  2. 1.2Working memory and its capacity
  3. 1.3Selective, divided and sustained attention
  4. 1.4Decision-making and heuristics
  5. 1.5Cognitive biases in operations
  6. 1.6Wickens' multiple resource model
  7. 1.7Rasmussen's SRK models
  8. 1.8Expertise and naturalistic decision-making
  9. 1.9Individual cognitive differences
  10. 1.10Case study: decision errors in a control centre
  1. 2.1Organisation of the nervous system
  2. 2.2Attentional networks of the brain
  3. 2.3Neurophysiology of mental workload
  4. 2.4Neurotransmitters, fatigue and alertness
  5. 2.5Plasticity and learning
  6. 2.6Emotion and performance
  7. 2.7Functional neuroimaging: principles
  8. 2.8Limitations of neural inference
  9. 2.9Ethics of neurotechnology
  10. 2.10Practical exercise: review of a neuroergonomics study
  1. 3.1Fundamentals of the EEG signal
  2. 3.2Dry and portable EEG systems
  3. 3.3Montages and the 10-20 system
  4. 3.4Pre-processing and artefact removal
  5. 3.5Event-related potentials
  6. 3.6Spectral indices of workload and fatigue
  7. 3.7Connectivity analysis
  8. 3.8EEG in real-world settings
  9. 3.9Reliability and validity of indices
  10. 3.10Lab: EEG during an n-back task
  1. 4.1Principles of near-infrared spectroscopy
  2. 4.2Prefrontal cortical haemodynamics
  3. 4.3Designing studies with fNIRS
  4. 4.4Pre-processing of haemodynamic signals
  5. 4.5Comparison with fMRI
  6. 4.6Electrocardiogram and heart rate variability
  7. 4.7Electrodermal response and emotional arousal
  8. 4.8Pupillometry and cognitive load
  9. 4.9Combining EEG, fNIRS and pupillometry
  10. 4.10Practical exercise: fNIRS in a vigilance task
  1. 5.1Definitions and models of mental workload
  2. 5.2ISO 10075 terminology and principles
  3. 5.3Subjective measures: NASA-TLX and SWAT
  4. 5.4Primary and secondary task performance measures
  5. 5.5Physiological measures and their sensitivity
  6. 5.6Overload and underload
  7. 5.7Workload in monitoring tasks
  8. 5.8Convergent validation of measures
  9. 5.9Designing workload experiments
  10. 5.10Workshop: multimodal workload assessment protocol
  1. 6.1Vigilance decrement
  2. 6.2Signal detection theory
  3. 6.3Mental fatigue and its measurement
  4. 6.4Microsleeps and attention lapses
  5. 6.5Distraction and interruptions
  6. 6.6Circadian rhythms and performance
  7. 6.7Stimulants and countermeasures
  8. 6.8Task design to sustain attention
  9. 6.9Real-time monitoring
  10. 6.10Lab: PVT task and lapse detection
  1. 7.1Ecological interface design
  2. 7.2Visualisation of complex information
  3. 7.3Alarm management and prioritisation
  4. 7.4Workload-adaptive interfaces
  5. 7.5Representations and mental models
  6. 7.6Decision aids
  7. 7.7Interaction with AI and explainability
  8. 7.8Cognitive evaluation of interfaces
  9. 7.9Interface errors and their prevention
  10. 7.10Practical exercise: ecological redesign of a process panel
  1. 8.1Architecture of closed-loop neuroadaptive systems
  2. 8.2Passive brain-computer interfaces
  3. 8.3Classification of mental states
  4. 8.4Machine learning with neural signals
  5. 8.5Real-time latency and reliability
  6. 8.6Applications in aviation and driving
  7. 8.7Applications in training
  8. 8.8Risks and limits of adaptation
  9. 8.9Privacy of neural data
  10. 8.10Lab: EEG-based workload classifier
  1. 9.1Neuroergonomics in aviation and air traffic control
  2. 9.2Driving and automated vehicles
  3. 9.3Industrial control rooms
  4. 9.4Surgery and healthcare settings
  5. 9.5Cybersecurity and SOC analysts
  6. 9.6Defence and critical operations
  7. 9.7Offices and knowledge work
  8. 9.8Education and simulation
  9. 9.9Sport and high performance
  10. 9.10Case study: cognitive workload in air traffic controllers
  1. 10.1Within- and between-subjects experimental design
  2. 10.2Sample size and power
  3. 10.3Repeated-measures ANOVA and its assumptions
  4. 10.4Linear mixed-effects models
  5. 10.5Pre-registration and open science
  6. 10.6Laboratory versus field
  7. 10.7High-fidelity simulation
  8. 10.8Reproducibility of biomarkers
  9. 10.9Reporting results
  10. 10.10Practical exercise: analysis of an experiment with mixed models
  1. 11.1Neurorights and the international debate
  2. 11.2GDPR and biometric data
  3. 11.3Consent in neural studies
  4. 11.4Use of neurotechnology at work
  5. 11.5OECD Recommendation on neurotechnology
  6. 11.6Medical devices versus consumer products
  7. 11.7Safety of EEG devices
  8. 11.8Ethics committee approval in neuroscience
  9. 11.9Responsible communication of results
  10. 11.10Case study: ethical assessment of an EEG headset for the workplace
  1. 12.1Neuroergonomic research question
  2. 12.2Literature review
  3. 12.3Experiment design
  4. 12.4Selection of measures and equipment
  5. 12.5Data collection
  6. 12.6Signal processing
  7. 12.7Statistical analysis of neural measures
  8. 12.8Implications for design
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online learning on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within each intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is EEGLAB, MNE-Python, Homer3, PsychoPy and Tobii Pro Lab, in free, academic or evaluation versions. Testing is studied through real procedures: EEG and fNIRS experiments in mental workload tasks, vigilance tasks with physiological measures and interface evaluation in simulators. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: ISO 10075, ISO 11064, ISO 9241-210, Regulation (EU) 2016/679 (GDPR), WMA Declaration of Helsinki, OECD Recommendation on Responsible Innovation in Neurotechnology, ISO 6385.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 10075, ISO 11064 and ISO 9241-210 applied to real cases, not as a theoretical annex.
Industry software:EEGLAB, MNE-Python, Homer3, PsychoPy and Tobii Pro Lab, in free, academic or evaluation versions.
Master's Thesis with defence:A neuroergonomics experiment, from research question to signal processing, statistical analysis and design implications, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Research question
Define a neuroergonomic research question backed by a literature review.
Experiment design
Design the experiment and select the measures and equipment.
Data collection
Collect EEG, fNIRS or physiological data from participants.
Signal processing and analysis
Process the signals and run the statistical analysis of the neural measures.
Master's Thesis
Draw implications for design in your Master's Thesis, defended before a panel.
Career opportunities

Where this takes you

Neuroergonomics researcherCognitive human factors engineerAdaptive interface specialistNeurophysiological data analystDecision-support system designerCognitive ergonomics consultant
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with EEGLAB, MNE-Python, Homer3, PsychoPy and Tobii Pro Lab, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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