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

Abstract

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.

Impacts of complex electromagnetic radiation and low-frequency noise exposure conditions on the cognitive function of operators

“…MWL is the result of a combination of factors, such as the level of effort a person exerts during an assignment and physiological and psychological demands during the assignment (19,20), which is influenced by a combination of intrinsic mental stress and extrinsic environmental factors (21). Moreover, MWL is one of the most widely used concepts in human ergonomics research and practice (22,23) and can be used to scientifically assess how well an operator performs current occupational operational tasks such as flying and driving (24,25). Additionally, functional nearinfrared spectroscopy (fNIRS) has been used for non-invasive monitoring of operators' brain function during a variety of operational tasks, as it allows for more objective and sensitive measurement and assessment of the level of MWL than other methods, providing an important research method for brain and cognitive science (25)(26)(27)(28).…”

“…MWL is the result of a combination of factors, such as the level of effort a person exerts during an assignment and physiological and psychological demands during the assignment ( 19 , 20 ), which is influenced by a combination of intrinsic mental stress and extrinsic environmental factors ( 21 ). Moreover, MWL is one of the most widely used concepts in human ergonomics research and practice ( 22 , 23 ) and can be used to scientifically assess how well an operator performs current occupational operational tasks such as flying and driving ( 24 , 25 ). Additionally, functional near-infrared spectroscopy (fNIRS) has been used for non-invasive monitoring of operators’ brain function during a variety of operational tasks, as it allows for more objective and sensitive measurement and assessment of the level of MWL than other methods, providing an important research method for brain and cognitive science ( 25–28 ).…”

Sensors and Actuation Technologies in Exoskeletons: A Review

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Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task

“…Additionally, exoskeleton robots can also be controlled using a performance-based method, in which assistive forces are adjusted to support the user's movement based on his or her motor performance (e.g., InMotion 2.0 system) (Krebs et al, 2003 ). Brain-controlled exoskeletons have been used as brain-computer interfaces (BCIs) in assistive exoskeleton robots to decode brain processes from brain signals, such as electroencephalography (EEG) (Hong and Khan, 2017 ; Choi et al, 2020 ) or hemodynamic signals (Khan and Hong, 2017 ; Asgher et al, 2021 ), and convert them into output motor commands (e.g., BCI-Manus system). Determining the effects of robot-assisted rehabilitation requires a deeper understanding of the mechanisms underlying human-robot interactions.…”

“…Movement-related cortical potentials have also been successfully decoded from EEG-based signals in the rmPFC (Min et al, 2017 ; Koizumi et al, 2018 ). These signals are useful in controlling brain-controlled exoskeletons designed to augment the user's sensorimotor functions (Agashe et al, 2016 ; Hong and Khan, 2017 ; Khan and Hong, 2017 ; Liu et al, 2018 ; Asgher et al, 2021 ). Moreover, a limited number of studies have been conducted to investigate the effect of robot-assisted tasks on cortical reorganization (Youssofzadeh et al, 2016 ; Saita et al, 2017 , 2018 ; Memar and Esfahani, 2018 ; Berger et al, 2019 ; Peters et al, 2020 ).…”