Functional near-infrared spectroscopy in the evaluation of urban rail transit drivers’ mental workload under simulated driving conditions

AbstractOptimal mental workload plays a key role in driving performance. Thus, driver-assisting systems that automatically adapt to a drivers current mental workload via brain–computer interfacing might greatly contribute to traffic safety. To design economic brain computer interfaces that do not compromise driver comfort, it is necessary to identify brain areas that are most sensitive to mental workload changes. In this study, we used functional near-infrared spectroscopy and subjective ratings to measure mental workload in two virtual driving environments with distinct demands. We found that demanding city environments induced both higher subjective workload ratings as well as higher bilateral middle frontal gyrus activation than less demanding country environments. A further analysis with higher spatial resolution revealed a center of activation in the right anterior dorsolateral prefrontal cortex. The area is highly involved in spatial working memory processing. Thus, a main component of drivers’ mental workload in complex surroundings might stem from the fact that large amounts of spatial information about the course of the road as well as other road users has to constantly be upheld, processed and updated. We propose that the right middle frontal gyrus might be a suitable region for the application of powerful small-area brain computer interfaces.

Download Full-text

Examining Mental Workload in a Spatial Navigation Transfer Game via Functional near Infrared Spectroscopy

Brain Sciences ◽

10.3390/brainsci11010045 ◽

2021 ◽

Vol 11 (1) ◽

pp. 45

Author(s):

Tamara Galoyan ◽

Kristen Betts ◽

Hovag Abramian ◽

Pratusha Reddy ◽

Kurtulus Izzetoglu ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Cognitive Load ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Mental Workload ◽

Spatial Navigation ◽

Difficulty Level ◽

Subjective Rating ◽

Functional Near Infrared Spectroscopy ◽

Nasa Tlx

The goal of this study was to examine the effects of task-related variables, such as the difficulty level, problem scenario, and experiment week, on performance and mental workload of 27 healthy adult subjects during problem solving within the spatial navigation transfer (SNT) game. The study reports task performance measures such as total time spent on a task (TT) and reaction time (RT); neurophysiological measures involving the use of functional near-infrared spectroscopy (fNIRS); and a subjective rating scale for self-assessment of mental workload (NASA TLX) to test the related hypothesis. Several within-subject repeated-measures factorial ANOVA models were developed to test the main hypothesis. The results revealed a number of interaction effects for the dependent measures of TT, RT, fNIRS, and NASA TLX. The results showed (1) a decrease in TT and RT across the three levels of difficulty from Week 1 to Week 2; (2) an increase in TT and RT for high and medium cognitive load tasks as compared to low cognitive load tasks in both Week 1 and Week 2; (3) an overall increase in oxygenation from Week 1 to Week 2. These findings confirmed that both the behavioral performance and mental workload were sensitive to task manipulations.

Download Full-text

Pre-Frontal Cortex Activity of Male Drivers in the Presence of Passengers During Simulated Driving: An Exploratory Functional Near- Infrared Spectroscopy (fNIRS) Study

10.17077/drivingassessment.1550 ◽

2015 ◽

Cited By ~ 2

Author(s):

Anuj K Pradhan ◽

Xiao-Su Frank Hu ◽

Lisa Buckley ◽

C Raymond Bingham

Keyword(s):

Infrared Spectroscopy ◽

Frontal Cortex ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Functional Near Infrared Spectroscopy ◽

Simulated Driving

Download Full-text

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.605751 ◽

2021 ◽

Vol 15 ◽

Author(s):

Umer Asgher ◽

Muhammad Jawad Khan ◽

Muhammad Hamza Asif Nizami ◽

Khurram Khalil ◽

Riaz Ahmad ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Mental Workload ◽

Assistive Technologies ◽

Brain Machine Interface ◽

Functional Near Infrared Spectroscopy ◽

Robotic Exoskeleton ◽

Machine Interface ◽

The Brain

Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier—support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.

Download Full-text