From Subjective Questionnaires to Saccadic Peak Velocity: A Neuroergonomics Index for Online Assessment of Mental Workload

Marek, Tadeusz; Karwowski, Waldemar; Rice, Valerie

doi:10.1201/ebk1439835012-4

Cited by 33 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Workload has been of interest in the fNIRS community, as well. Cognitive workload has been assessed for air-traffic controllers in several studies Ayaz et al ( 2010 , 2012 ). Izzetoglu et al ( 2003 ) show that task load in the Warship Commander tasks yield distinct hemodynamic responses on average.…”

Section: Introductionmentioning

confidence: 99%

Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

Herff

Heger

Fortmann

et al. 2014

Front. Hum. Neurosci.

273

214

View full text Add to dashboard Cite

When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g., interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to elicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user. The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}) to induce different levels of workload, forcing subjects to continuously remember the last one, two, or three of rapidly changing items. Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload. Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online.

show abstract

Section: Introductionmentioning

confidence: 99%

Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

Herff

Heger

Fortmann

et al. 2014

Front. Hum. Neurosci.

273

214

View full text Add to dashboard Cite

show abstract

“…Predictive models have been used to differentiate the fNIRS signal between levels of workload [4,20,28,34], verbal and spatial working memory [19], and game difficulty levels [17]. Furthermore, it has been used to determine periods of cognitive multitasking [36,37], levels of expertise [7], preference [25,29], and emotion [40].…”

Section: Fnirs and Prefrontal Cortexmentioning

confidence: 99%

Dynamic difficulty using brain metrics of workload

Afergan

Peck

Solovey

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

119

View full text Add to dashboard Cite

Dynamic difficulty adjustments can be used in humancomputer systems in order to improve user engagement and performance. In this paper, we use functional near-infrared spectroscopy (fNIRS) to obtain passive brain sensing data and detect extended periods of boredom or overload. From these physiological signals, we can adapt a simulation in order to optimize workload in real-time, which allows the system to better fit the task to the user from moment to moment. To demonstrate this idea, we ran a laboratory study in which participants performed path planning for multiple unmanned aerial vehicles (UAVs) in a simulation. Based on their state, we varied the difficulty of the task by adding or removing UAVs and found that we were able to decrease errors by 35% over a baseline condition. Our results show that we can use fNIRS brain sensing to detect task difficulty in real-time and construct an interface that improves user performance through dynamic difficulty adjustment.

show abstract

“…Relying on simple and cheap fundamental technology, fNIRS is safe, comfortable, easy-to-setup, and has the potential for portability. The data from the device has been used to differentiate between levels of workload [3,25,37,42], verbal and spatial working memory [24], game difficulty levels [16], and cognitive multitasking. Putting this data to practical use, predictive models have been applied in real time to adapt user interfaces to these physiological states [45,46].…”

Section: Brain Sensing With Fnirsmentioning

confidence: 99%

Brain-based target expansion

Afergan

Shibata

Hincks

et al. 2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

View full text Add to dashboard Cite

The bubble cursor is a promising cursor expansion technique, improving a user's movement time and accuracy in pointing tasks. We introduce a brain-based target expansion system, which improves the efficacy of bubble cursor by increasing the expansion of high importance targets at the optimal time based on brain measurements correlated to a particular type of multitasking. We demonstrate through controlled experiments that brain-based target expansion can deliver a graded and continuous level of assistance to a user according to their cognitive state, thereby improving task and speed-accuracy metrics, even without explicit visual changes to the system. Such an adaptation is ideal for use in complex systems to steer users toward higher priority goals during times of increased demand.

show abstract

From Subjective Questionnaires to Saccadic Peak Velocity: A Neuroergonomics Index for Online Assessment of Mental Workload

Cited by 33 publications

References 1 publication

Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

Dynamic difficulty using brain metrics of workload

Brain-based target expansion

Contact Info

Product

Resources

About