Neuroergonomics

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Neuroergonomics is the application of neuroscience to ergonomics. Traditional ergonomic studies rely predominantly on psychological explanations to address human factors issues such as: work performance, operational safety, and workplace-related risks (e.g., repetitive stress injuries). Neuroergonomics, in contrast, addresses the biological substrates of ergonomic concerns, with an emphasis on the role of the human nervous system.

Contents

Overview

Neuroergonomics has two major aims: to use existing/emerging knowledge of human performance and brain function to design systems for safer and more efficient operation, and to advance this understanding of the relationship between brain function and performance in real-world tasks.

To meet these goals, neuroergonomics combines two disciplines—neuroscience, the study of brain function, and human factors, the study of how to match technology with the capabilities and limitations of people so they can work effectively and safely. The goal of merging these two fields is to use the startling discoveries of human brain and physiological functioning both to inform the design of technologies in the workplace and home, and to provide new training methods that enhance performance, expand capabilities, and optimize the fit between people and technology.

Research in the area of neuroergonomics has blossomed in recent years with the emergence of noninvasive techniques for monitoring human brain function that can be used to study various aspects of human behavior in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation. Consequently, this interdisciplinary field is concerned with investigations of the neural bases of human perception, cognition, and performance in relation to systems and technologies in the real world—for example, in the use of computers and various other machines at home or in the workplace, and in operating vehicles such as aircraft, cars, trains, and ships.

Approaches

Functional neuroimaging

A central goal of neuroergonomics is to study the way in which brain function is related to task/work performance. To do this, noninvasive neuroimaging methods are typically used to record direct neurophysiological markers of brain activity through electrical activity electroencephalography (EEG), magnetoencephalography (MEG) or through indirect metabolic positron-emission tomography (PET) and neurovascular measures of neural activity including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), transcranial Doppler (TCD) sonography. Typically, neuroergonomic studies are more application-oriented than basic cognitive neuroscience studies and often require a balance between controlled environments and naturalistic settings. Studies using larger room-scale neuroimaging setups such as PET, MEG, and fMRI, offer increased spatial and temporal resolution at the expense of increased restrictions on participants actions. Using more mobile techniques such as fNIRS and EEG, research may be conducted in more realistic settings including even participation in the actual work being investigated (ex: driving). These techniques have the advantage of being more affordable and versatile, but may also compromise by reducing the number of areas recorded and the ability to image neural activity from deeper brain regions. Together the application of both controlled lab experiments and the translation of findings in realistic contexts represents the spectrum of neuroimaging in neuroergonomics.

Neurostimulation

Neurostimulation methods may also be used apart, or in conjunction with neuroimaging approaches to probe the involvement of cortical regions in task performance. Techniques such as transcranial magnetic stimulation (TMS) and transcranial direct-current stimulation (tDCS) can be used to temporarily alter the excitability of cortical regions. It is proposed that stimulating a cortical region (particularly with TMS) can disrupt or enhance that regions function, permitting researchers to test specific hypotheses related to human performance.

Some studies have shown the promise of using TMS and tDCS to improve cognitive skills during tasks. While initially used to treat various neurological disorders such as Parkinson's disease or dementia, the scope of TMS is expanding. In TMS, electricity is passed through a magnetic coil that is positioned near the person's scalp. Results from studies show that noninvasive brain stimulation leads to 20 more minutes of sustained vigilance performance. [1]

Psychophysiology

Psychophysiological measures are physiological measures (blood, heart rate, skin conductance, etc.) which change as part of psychological processes. Although not considered as a direct neural measure, neuroergonomics also promotes the use of physiological correlates as dependent measures when they can serve as an index of neural activities such as attention, motor, or affective processes. These measures can be used in conjunction with neuroimaging measures, or as a substitute when the acquisition of neuroimaging measures is too costly, dangerous, or otherwise impractical. Psychophysiology is a distinct field from neuroergonomics; however, the principals and objectives can be considered complementary.

Applications

Mental Workload Assessment

Using an fMRI, mental workload can be quantified by an increase in cerebral blood flow in regions of the prefrontal cortex (PFC). Many fMRI studies show that there is increased PFC activation during a working memory task. Equally important as measuring mental workload, is evaluating the operator vigilance, or attentiveness. Using TCD to monitor blood flow velocity in intercranial arteries, it was shown that a decrease in blood flow was associated with a decrease in vigilance and depletion of cognitive resources. [2]

Adaptive Automation

Adaptive automation, a novel neuroergonomic concept, refers to a human-machine system that uses real-time assessment of the operator's workload to make the necessary changes to enhance performance. For adaptive automation to work, the system must utilize an accurate operator-state classifier for the real-time assessment. Operator-state classifiers such as discriminant analysis and artificial neural networks show an accuracy of 70% to 85% in real-time. An important part to properly implementing adaptive automation is figuring out how big a workload needs to be to require intervention. Implementing neuroergonomic adaptive automation would require the development of nonintrusive sensors and even techniques to track eye movement. Current research into assessing a person's mental state includes using facial electromyography to detect confusion. [3]

Experiments show that a human-robot team performs better at controlling air and ground vehicles than either a human or robot (i.e. the automatic target recognition system). When compared to 100% human control and static automation, participants showed higher trust and self-confidence, as well as lower perceived workload, when using adaptive automation. [4]

In adaptive automation, getting the machine to accurately reason how to respond to the changes and get back to peak performance is the biggest challenge. The machine has to be able to determine to what extent it must make the changes. This is also a consequence of the complexity of the system and factors such as: how easily can the sensed parameter be quantified, how many parameters in the machine's system can be changed, and how well can these different machine parameters be coordinated.

Brain Computer Interfaces

A developing area of research called brain–computer interfaces (BCIs) strives to use different types of brain signals to operate external devices, without any motor input from the person. BCIs provide promise for patients with limited motor capabilities, such as those with amyotrophic lateral sclerosis. When the user engages in a specific mental activity, it generates a unique brain electrical potential that is processed and relayed into a signal for the external device. BCIs using signals from EEGs and ERPs have been used to operate voice synthesizers and move robotic arms. Research for BCIs began in the 1970s at the University of California Los Angeles, and its current focus is towards neuroprosthetic applications. BCIs can be substantially improved by incorporating high-level control, context, the environment, as well as virtual reality into its design. [5]

Stroke Rehabilitation

As of 2011, there has been an effort to applying a rehabilitation robot connected to a non-invasive brain–computer interface to promote brain plasticity and motor learning following a stroke. Half of stroke survivors experience unilateral paralysis or weakness, and approximately 30-60% of them do not regain function. Typical treatment, post-stroke, involves constraint-induced movement therapy and robotic therapy, which work to restore motor activity by forcing the movement of the weak limbs. Current active therapy cannot be utilized by patients who suffer complete control loss or paralysis, and do not have any residual motor ability to work with.

With a focus on these underserved patients, a BCI was created that used the electrical brain signals detected by an EEG to control an upper-limb rehabilitative robot. The user is instructed to imagine the motor activity while the EEG picks up the associated brain signals. The BCI uses a linear transformation algorithm to convert the EEG spectral features into commands for the robot. An experiment done on 24 subjects tested a non-BCI group, which used sensorimotor rhythms to control the robot, against the BCI-group, which used the BCI-robot system. The results from the brain-plasticity analysis showed that there was a decrease in beta wave activity in the subjects of the BCI-group, which is associated with a change in movement. The results also showed that the BCI-group performed better than the non-BCI group in every measure for motor learning. [6] [ undue weight? – discuss ]

Virtual Reality

Virtual reality could allow for testing how human operators would work in dangerous environments without actually putting them in harm's way. For example, it would allow the testing of how fatigue or a new technology would affect a driver or a pilot in their specific environment, without the possibility of injury. Being able to evaluate the effects of some new workplace technology in virtual reality, before real life implementation, could save money and lives. Bringing virtual reality technology to the point where it can accurately mimic real life is difficult, but its potential is vast. [7]

Healthcare Training

Healthcare training programs have adopted virtual reality simulation (VRS) as a training tool for nursing students. This computer-based three-dimensional simulation tool allows for nursing students to practice various nursing skills repeatedly in a risk-free environment. A nursing program at a major Midwestern state university agreed to utilize a VRS module for teaching the insertion of an intravenous (IV) catheter, and complete an evaluation on the effectiveness of the program. The VRS composed of a computer program and a haptic arm device, which worked together to simulate the feel of vascular access. On the computer screen, the user would have to select the equipment for the procedure in the correct order. The user then palpates the veins of the haptic arm, and virtually inserts the IV catheter. The program provides immediate feedback by notifying the user when he/she misses a step and needs to restart the procedure.

Results of evaluation pointed to the VRS an "excellent learning tool" for increasing a student's knowledge on the procedure. All eight of the nursing faculty who participated agreed to this much, and that they would recommend that students work with the VRS before performing the IV catheter insertion on real patients.

This tool allows educators to expose students to an extensive range of real-life patient conditions and nursing experiences. The central advantage of the VRS program is the availability of a variety of case scenarios, which allow students to increase their awareness of differences in patient responses to IV catheter insertion. From the standpoint of the student, the virtual reality simulation helps bridge the gap between nursing theory and practice. [8] [ undue weight? – discuss ]

Applications for Neurocognitive Disabilities

Neuroergonomic assessments have tremendous potential for evaluating the psychomotor performance in an individual with a neurocognitive disability or following a stroke or surgery. They would allow for a standardized method for measuring the change in neurocognitive function during rehabilitation for a neurocognitive disability. In terms of rehabilitation, it would allow for the efforts to be goal-oriented. These tests could be applied for measuring change following operational procedures such as neurosurgery, carotid endarterectomy, and coronary artery bypass graft. [9]

Driving Safety

One of the main application domains of neuroergonomics is driving safety, especially for older drivers with cognitive impairments. Driving requires the integration of multiple cognitive processes, which can be studied separately if the right kinds of tools are used. The types of tools used to evaluate cognition during driving include driving simulators, instrumented vehicles, and part task simulators. [10]

The Crossmodal Research Laboratory in Oxford is working on developing a system of warning signals to grab the attention of a distracted driver, in an effort to make driving safer for everybody. The research has found that using auditory icons, such as a car horn, is a better warning signal than a pure tone. On top of that, spatial auditory cues work better at redirecting the driver's attention than non-spatialized auditory cues. Cues that integrate multiple senses, such as an audiotactile signal, grab attention better than unisensory cues. [11] Others have evaluated different types of in-vehicle notifications (i.e., auditory icons, speech commands) designed for task management in autonomous trucks for their relevance to separable neural mechanisms; this serves as an effective method to clarify often conflicting findings drawn from behavioral results alone. [12]

Related Research Articles

Rehabilitation of sensory and cognitive function typically involves methods for retraining neural pathways or training new neural pathways to regain or improve neurocognitive functioning that have been diminished by disease or trauma. The main objective outcome for rehabilitation is to assist in regaining physical abilities and improving performance. Three common neuropsychological problems treatable with rehabilitation are attention deficit/hyperactivity disorder (ADHD), concussion, and spinal cord injury. Rehabilitation research and practices are a fertile area for clinical neuropsychologists, rehabilitation psychologists, and others.

<span class="mw-page-title-main">Functional neuroimaging</span>

Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.

<span class="mw-page-title-main">Brain–computer interface</span> Direct communication pathway between an enhanced or wired brain and an external device

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine. Implementations of BCIs range from non-invasive and partially invasive to invasive, based on how close electrodes get to brain tissue.

Neurotechnology encompasses any method or electronic device which interfaces with the nervous system to monitor or modulate neural activity.

Cognitive ergonomics is a scientific discipline that studies, evaluates, and designs tasks, jobs, products, environments and systems and how they interact with humans and their cognitive abilities. It is defined by the International Ergonomics Association as "concerned with mental processes, such as perception, memory, reasoning, and motor response, as they affect interactions among humans and other elements of a system. Cognitive ergonomics is responsible for how work is done in the mind, meaning, the quality of work is dependent on the persons understanding of situations. Situations could include the goals, means, and constraints of work. The relevant topics include mental workload, decision-making, skilled performance, human-computer interaction, human reliability, work stress and training as these may relate to human-system design." Cognitive ergonomics studies cognition in work and operational settings, in order to optimize human well-being and system performance. It is a subset of the larger field of human factors and ergonomics.

Social neuroscience is an interdisciplinary field devoted to understanding the relationship between social experiences and biological systems. Humans are fundamentally a social species, rather than solitary. As such, Homo sapiens create emergent organizations beyond the individual—structures that range from dyads, families, and groups to cities, civilizations, and cultures. In this regard, studies indicate that various social influences, including life events, poverty, unemployment and loneliness can influence health related biomarkers. The term "social neuroscience" can be traced to a publication entitled "Social Neuroscience Bulletin" which was published quarterly between 1988 and 1994. The term was subsequently popularized in an article by John Cacioppo and Gary Berntson, published in the American Psychologist in 1992. Cacioppo and Berntson are considered as the legitimate fathers of social neuroscience. Still a young field, social neuroscience is closely related to personality neuroscience, affective neuroscience and cognitive neuroscience, focusing on how the brain mediates social interactions. The biological underpinnings of social cognition are investigated in social cognitive neuroscience.

<span class="mw-page-title-main">Mu wave</span> Electrical activity in the part of the brain controlling voluntary movement

The sensorimotor mu rhythm, also known as mu wave, comb or wicket rhythms or arciform rhythms, are synchronized patterns of electrical activity involving large numbers of neurons, probably of the pyramidal type, in the part of the brain that controls voluntary movement. These patterns as measured by electroencephalography (EEG), magnetoencephalography (MEG), or electrocorticography (ECoG), repeat at a frequency of 7.5–12.5 Hz, and are most prominent when the body is physically at rest. Unlike the alpha wave, which occurs at a similar frequency over the resting visual cortex at the back of the scalp, the mu rhythm is found over the motor cortex, in a band approximately from ear to ear. People suppress mu rhythms when they perform motor actions or, with practice, when they visualize performing motor actions. This suppression is called desynchronization of the wave because EEG wave forms are caused by large numbers of neurons firing in synchrony. The mu rhythm is even suppressed when one observes another person performing a motor action or an abstract motion with biological characteristics. Researchers such as V. S. Ramachandran and colleagues have suggested that this is a sign that the mirror neuron system is involved in mu rhythm suppression, although others disagree.

Developmental cognitive neuroscience is an interdisciplinary scientific field devoted to understanding psychological processes and their neurological bases in the developing organism. It examines how the mind changes as children grow up, interrelations between that and how the brain is changing, and environmental and biological influences on the developing mind and brain.

Brain-reading or thought identification uses the responses of multiple voxels in the brain evoked by stimulus then detected by fMRI in order to decode the original stimulus. Advances in research have made this possible by using human neuroimaging to decode a person's conscious experience based on non-invasive measurements of an individual's brain activity. Brain reading studies differ in the type of decoding employed, the target, and the decoding algorithms employed.

Neuronavigation is the set of computer-assisted technologies used by neurosurgeons to guide or "navigate" within the confines of the skull or vertebral column during surgery, and used by psychiatrists to accurately target rTMS. The set of hardware for these purposes is referred to as a neuronavigator.

<span class="mw-page-title-main">Wellcome Centre for Human Neuroimaging</span> Laboratory of the University College London

The Wellcome Centre for Human Neuroimaging at University College London is a world-leading interdisciplinary centre for neuroimaging research based in London, United Kingdom. Researchers at the Centre use expertise to investigate how the human brain generates behaviour, thoughts and feelings and how to use this knowledge to help patients with neurological and psychiatric disorders. Human neuroimaging allows scientists to non-invasively investigate the brain structure and functions including Action, Decision Making, Emotion, Hearing, Language, Memory, Navigation, Seeing, Self awareness, Social Behaviour and the Bayesian Brain

The neuroscience of music is the scientific study of brain-based mechanisms involved in the cognitive processes underlying music. These behaviours include music listening, performing, composing, reading, writing, and ancillary activities. It also is increasingly concerned with the brain basis for musical aesthetics and musical emotion. Scientists working in this field may have training in cognitive neuroscience, neurology, neuroanatomy, psychology, music theory, computer science, and other relevant fields.

In modern psychology, vigilance, also termed sustained concentration, is defined as the ability to maintain concentrated attention over prolonged periods of time. During this time, the person attempts to detect the appearance of a particular target stimulus. The individual watches for a signal stimulus that may occur at an unknown time.

The following outline is provided as an overview of and topical guide to brain mapping:

A cortical implant is a subset of neuroprosthetics that is in direct connection with the cerebral cortex of the brain. By directly interfacing with different regions of the cortex, the cortical implant can provide stimulation to an immediate area and provide different benefits, depending on its design and placement. A typical cortical implant is an implantable microelectrode array, which is a small device through which a neural signal can be received or transmitted.

The Cognition and Neuroergonomics (CaN) Collaborative Technology Alliance was a research program initiated, sponsored and partly performed by the U.S. Army Research Laboratory. The objective of the program was to “conduct research and development leading to the demonstration of fundamental translational principles of the application of neuroscience-based research and theory to complex operational settings. These principles will guide the development of technologies that work in harmony with the capabilities and limitations of the human nervous system.”

Cassandra J. Lowe is a Canadian public health neuroscientist, specializing in understanding why some individuals have a hard time regulating junk food consumption. Lowe uses a multidimensional approach that combines repetitive transcranial magnetic stimulation (rTMS), neuroimaging and aerobic exercise to create causal models linking brain health to dietary decisions and behaviours. She was formally a BrainsCAN Postdoctoral Fellow within The Brain and Mind Institute and Department of Psychology at the University of Western Ontario, working with Dr. J. Bruce Morton and Dr. Lindsay Bodell. Since 2022, she has undertaken a position at the University of Exeter, in the United Kingdom as Lecturer in the School of Psychology.

Valeria Gazzola is an Italian neuroscientist, associate professor at the Faculty of Social and Behavioral Sciences at the University of Amsterdam (UvA) and member of the Young Academy of Europe. She is also a tenured department head at the Netherlands Institute for Neuroscience (NIN) in Amsterdam, where she leads her own research group and the Social Brain Lab together with neuroscientist Christian Keysers. She is a specialist in the neural basis of empathy and embodied cognition: Her research focusses on how the brain makes individuals sensitive to the actions and emotions of others and how this affects decision-making.

<span class="mw-page-title-main">Alexander T. Sack</span>

Alexander T. Sack is a German neuroscientist and cognitive psychologist. He is currently appointed as a full professor and chair of applied cognitive neuroscience at the Faculty of Psychology and Neuroscience at Maastricht University. He is also co-founder and board member of the Dutch-Flemish Brain Stimulation Foundation, director of the International Clinical TMS Certification Course, co-director of the Center for Integrative Neuroscience (CIN) and the Scientific Director of the Transcranial Brain Stimulation Policlinic at Maastricht University Medical Centre.

<span class="mw-page-title-main">Thorsten O. Zander</span> German scientist (born 1975)

Thorsten O. Zander is a German scientist who introduced the concept of passive brain-computer interface. He co-founded Zander Labs, a German-Dutch company in the field of passive brain computer interface (pBCI) and neuro-adaptive technology (NAT).

References

  1. ↑ R. Andy, M., Nathaniel, B., Craig M., W., & Jeremy, N. (n.d). Modulating the brain at work using noninvasive transcranial stimulation. NeuroImage, 59(Neuroergonomics: The human brain in action and at work), 129-137.
  2. ↑ Parasuraman, R. (2008). "Putting the brain to work: Neuroergonomics past, present, and future". Human Factors, 50(3), 468-474.
  3. ↑ Durso, F. T. (2012). "Detecting Confusion Using Facial Electromyography". Human Factors, 54(1), 60-69.
  4. ↑ de Visser, E., & Parasuraman, R. (2011). Adaptive aiding of human-robot teaming: Effects of imperfect automation on performance, trust, and workload. Journal of Cognitive Engineering and Decision Making, 5(2), 209-231.
  5. ↑ Allison, B., Leeb, R., Brunner, C., Muller-Putz, G., Bauernfeind, G., Kelly, J., & Neuper, C. (n.d). Toward smarter BCIs: extending BCIs through hybridization and intelligent control. Journal of Neural Engineering, 9(1).
  6. ↑ Babalola, K. (2011). Brain–computer interfaces for inducing brain plasticity and motor learning: implications for brain-injury rehabilitation / by Karolyn Olatubosun Babalola. Atlanta, Ga. : Georgia Institute of Technology, 2011.
  7. ↑ Parasuraman, R., & Rizzo, M. (2007). Neuroergonomics: The brain at work. Oxford; New York: Oxford University Press.
  8. ↑ Jenson, C., & Forsyth, D. (2012). Virtual Reality Simulation: Using Three-dimensional Technology to Teach Nursing Students. Computers, Informatics, Nursing, 30(6), 312-318.
  9. ↑ Henry J., M., & David J., M. (n.d). Neurocognitive disability, stroke, and surgery: A role for neuroergonomics?. Journal of Psychosomatic Research, 63, 613-615.
  10. ↑ Lees, M. N., Cosman, J. D., Lee, J. D., Fricke, N., & Rizzo, M. (2010). Translating cognitive neuroscience to the driver's operational environment: A neuroergonomic approach. American Journal of Psychology, 123(4), 391-411.
  11. ↑ Spence, C. (2012). Drive safely with neuroergonomics. Psychologist, 25(9), 664-667.
  12. ↑ Glatz, C., Krupenia, S. S., Bülthoff, H. H., & Chuang, L. L. (2018, April). "Use the Right Sound for the Right Job: Verbal Commands and Auditory Icons for a Task-Management System Favor Different Information Processes in the Brain". Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 472, 1-10.

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