In some high-stakes jobs, the pressure can feel overwhelming. From air traffic controllers tracking dozens of aircraft simultaneously to surgeons guiding robotic instruments through delicate procedures, many modern occupations require people to process extraordinary amounts of information while maintaining accuracy and focus. As workplaces become more automated and technologically sophisticated, understanding how the brain and body respond to these demands has become a central challenge for ergonomics researchers. A growing area of study, known as neuroergonomics, combines neuroscience, physiology, and human factors research to better understand how people perform in complex real-world environments.
In human factors research, neuroergonomics conintues to gain traction by offering a more comprehensive picture of cognitive workload than traditional measures. Two individuals may perform a task equally well while experiencing dramatically different levels of mental effort. Excessive cognitive workload can contribute to fatigue, stress, errors, and reduced situational awareness, while insufficient workload can lead to disengagement and decreased vigilance. Researchers are therefore concentrating attention on measuring not only what people do but also how their brains and bodies respond as they do it.
Among the most valuable tools for studying cognitive workload are physiological signals that provide objective measurements of mental state. Electroencephalography (EEG) can reveal changes in neural activity associated with attention and cognitive effort, while functional near-infrared spectroscopy (fNIRS) measures changes in cortical blood oxygenation linked to mental workload. Electrocardiography (ECG) and heart rate variability (HRV) offer insight into autonomic nervous system activity, and eye-tracking measures such as pupil diameter and gaze behavior can indicate shifts in attention and cognitive demand. More often, researchers are combining multiple synchronized signals to create a more complete view of how people interact with demanding tasks and environments.
A team of researchers from Drexel University and Lockheed Martin investigated how multiple physiological signals can be combined to measure cognitive workload across a range of mental activities. Participants completed tasks involving working memory, vigilance, risk assessment, attention shifting, situation awareness, and inhibitory control while researchers simultaneously monitored EEG, fNIRS, ECG, photoplethysmography (PPG), electrooculography (EOG), and eye tracking. Brain activity was measured using an fNIR Devices system alongside other physiological monitoring technologies. The researchers found that different physiological measures varied in their sensitivity to specific task demands, with neuroimaging measures providing particularly valuable insight into changes in workload. The findings demonstrate the value of combining signals from the brain, heart, and eyes to create a broader perspective of cognitive effort in complex environments.
Researchers at Loughborough University in the United Kingdom examined how rising task complexity influences cognitive workload in modern manufacturing environments. Participants performed a series of assembly tasks while researchers recorded pupil diameter with Tobii eye-tracking and measured HRV with wearable ECG sensors. By comparing physiological responses across low-, medium-, and high-complexity tasks, the team identified significant changes in both pupillometry and cardiac indicators of cognitive load. Their findings suggest that physiological monitoring can provide an objective means of evaluating cognitive ergonomics in highly digitalized manufacturing environments and may help support the design of more sustainable, human-centered workplaces.
Another important challenge in neuroergonomics is determining which physiological measurements provide the most reliable indicators of mental workload. A collaborative team of researchers from France investigated how physiological measurements can be used to establish standardized levels of mental workload during aviation-inspired tasks. Using the Multi-Attribute Task Battery (MATB-II), participants performed simulated flight-related tasks under varying levels of demand while researchers measured EEG, eye-tracking, electrodermal activity, and cardiac activity. ECG and EDA signals were acquired using a BIOPAC data acquisition and analysis system running AcqKnowledge software. The study found that several cardiac, ocular, and electroencephalographic measures successfully distinguished high-workload conditions and correlated with subjective workload ratings. The researchers found that several physiological measures, particularly EEG, eye tracking, and cardiovascular metrics, reliably distinguished between different workload levels and closely tracked participants’ subjective assessments of task difficulty. Their findings suggest that multimodal physiological monitoring could support the development of adaptive systems that detect cognitive overload in real time, improving both performance and safety in demanding operational environments.
These studies highlight the increasingly multimodal nature of neuroergonomics research. Eye tracking provides insight into attention and visual processing, cardiovascular measures reveal how the autonomic nervous system responds to mental demands, and brain-imaging techniques such as EEG and fNIRS offer direct windows into neural workload. By combining these complementary perspectives, researchers are moving beyond simple measures of task performance toward a deeper understanding of how people think, adapt, and perform in complex environments. As workplaces continue to evolve alongside automation and intelligent technologies, neuroergonomics is helping researchers design systems that better support human capabilities, improve safety, and reduce the hidden costs of cognitive overload.
Are you planning a multimodal neuroergonomics study? Our team of experts can help you plan your research and identify the tools that will provide the best possible data.
