scholarly journals The Validation of a Portable Functional NIRS System for Assessing Mental Workload

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3810
Author(s):  
Manob Jyoti Saikia ◽  
Walter G. Besio ◽  
Kunal Mankodiya
Keyword(s):  
Cognitive Load ◽  
Near Infrared ◽  
Mental Workload ◽  
Brain Area ◽  
Experimental Studies ◽  
Functional Near Infrared Spectroscopy ◽  
Naturalistic Environment ◽  
The Right ◽  
User Friendly ◽  
The Brain

Portable functional near-infrared spectroscopy (fNIRS) systems have the potential to image the brain in naturalistic settings. Experimental studies are essential to validate such fNIRS systems. Working memory (WM) is a short-term active memory that is associated with the temporary storage and manipulation of information. The prefrontal cortex (PFC) brain area is involved in the processing of WM. We assessed the PFC brain during n-back WM tasks in a group of 25 college students using our laboratory-developed portable fNIRS system, WearLight. We designed an experimental protocol with 32 n-back WM task blocks with four different pseudo-randomized task difficulty levels. The hemodynamic response of the brain was computed from the experimental data and the evaluated brain responses due to these tasks. We observed the incremental mean hemodynamic activation induced by the increasing WM load. The left-PFC area was more activated in the WM task compared to the right-PFC. The task performance was seen to be related to the hemodynamic responses. The experimental results proved the functioning of the WearLight system in cognitive load imaging. Since the portable fNIRS system was wearable and operated wirelessly, it was possible to measure the cognitive load in the naturalistic environment, which could also lead to the development of a user-friendly brain–computer interface system.

scholarly journals Interpersonal brain synchronization under bluffing in strategic games

2020 ◽  
Vol 15 (12) ◽  
pp. 1326-1335
Author(s):  
Zhihao Wang ◽  
Yiwen Wang ◽  
Xiaolin Zhou ◽  
Rongjun Yu
Keyword(s):  
Near Infrared ◽  
Brain Activity ◽  
Strategic Thinking ◽  
Human Condition ◽  
Angular Gyrus ◽  
Strategic Games ◽  
Functional Near Infrared Spectroscopy ◽  
High Penalty ◽  
The Right ◽  
The Brain

Abstract People commonly use bluffing as a strategy to manipulate other people’s beliefs about them for gain. Although bluffing is an important part of successful strategic thinking, the inter-brain mechanisms underlying bluffing remain unclear. Here, we employed a functional near-infrared spectroscopy hyperscanning technique to simultaneously record the brain activity in the right temporal-parietal junction in 32 pairs of participants when they played a bluffing game against each other or with computer opponents separately. We also manipulated the penalty for bluffing (high vs low). Under the condition of high relative to low penalty, results showed a higher bluffing rate and a higher calling rate in human-to-human as compared to human-to-computer pairing. At the neural level, high relative to low penalty condition increased the interpersonal brain synchronization (IBS) in the right angular gyrus (rAG) during human-to-human as compared to human-to-computer interaction. Importantly, bluffing relative to non-bluffing, under the high penalty and human-to-human condition, resulted in an increase in response time and enhanced IBS in the rAG. Participants who bluffed more frequently also elicited stronger IBS. Our findings support the view that regions associated with mentalizing become synchronized during bluffing games, especially under the high penalty and human-to-human condition.

scholarly journals Shedding light on the prefrontal correlates of mental workload in simulated driving: a functional near-infrared spectroscopy study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph F. Geissler ◽  
Jörn Schneider ◽  
Christian Frings
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Spatial Information ◽  
Mental Workload ◽  
Middle Frontal Gyrus ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
The Right

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.

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

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.

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

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.

scholarly journals Using Functional Near Infrared Spectroscopy to Assess Cognitive Workload

2021 ◽  
Vol 6 (3) ◽  
pp. 197-204
Author(s):  
D Ravi ◽  
K. Ramachandran ◽  
Pushpendra Kumar Singh ◽  
Mistu Mahajabin
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Load ◽  
Human Performance ◽  
Near Infrared ◽  
Mental Workload ◽  
Binary Logistic Regression ◽  
Repeated Measure ◽  
Binary Logistic Regression Model ◽  
Functional Near Infrared Spectroscopy ◽  
Task Conditions

Quantification of mental workload is a significant aspect of monitoring and adaptive aiding systems that are intended to improve the efficiency and safety of human–machine systems. Functional near Infrared (fNIR) spectroscopy is a field-deployable brain monitoring device that provides a measures of cerebral hemodynamic within the prefrontal cortex. The purpose of this study was to assess the cognitive load by using Performance (reaction time), Behavioral metrics (NASA TLX) and Neuro-Cognitive Measures (Hemodynamic response). To observe the activation in prefrontal cortex, we employed Functional Near Infrared (fNIR) Spectroscopy with a Standard Stroop task. A total of 25 healthy participants (N 18 Male and N 07 Female, M Age 25.5 SD 7.6), participated in the study. For statistical analysis, a repeated measure t-test was computed to compare the Oxy (Δ[HbO2]) and De-Oxy (Δ[hHb]) changes under Congruent and In-Congruent task conditions. For Classification, Binary logistic regression model applied to identify how accurately classifying the varied workload conditions. The finding shows that fNIR measures had adequate predictive power for estimating task performance in workload conditions. In this paper, we have found evidence that fNIR can be used as indicator of cognitive load which is important for optimal human performance.

scholarly journals Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 460
Author(s):  
Yun-Hsuan Chen ◽  
Mohamad Sawan
Keyword(s):  
Risk Prediction ◽  
High Precision ◽  
Near Infrared ◽  
Stroke Risk ◽  
Functional Near Infrared Spectroscopy ◽  
Frequency Of Use ◽  
Data Continuity ◽  
User Friendly ◽  
Different Characteristics ◽  
The Internet Of Things

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

scholarly journals Comparison of Activation in the Prefrontal Cortex of Native Speakers of Mandarin by Ability of Japanese as a Second Language Using a Novel Speaking Task

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 412
Author(s):  
Li Cong ◽  
Hideki Miyaguchi ◽  
Chinami Ishizuki
Keyword(s):  
Second Language ◽  
Prefrontal Cortex ◽  
Cognitive Load ◽  
Near Infrared ◽  
Native Speakers ◽  
Brain Activation ◽  
Brain Regions ◽  
Strong Inhibition ◽  
Functional Near Infrared Spectroscopy ◽  
High Level

Evidence shows that second language (L2) learning affects cognitive function. Here in this work, we compared brain activation in native speakers of Mandarin (L1) who speak Japanese (L2) between and within two groups (high and low L2 ability) to determine the effect of L2 ability in L1 and L2 speaking tasks, and to map brain regions involved in both tasks. The brain activation during task performance was determined using prefrontal cortex blood flow as a proxy, measured by functional near-infrared spectroscopy (fNIRS). People with low L2 ability showed much more brain activation when speaking L2 than when speaking L1. People with high L2 ability showed high-level brain activation when speaking either L2 or L1. Almost the same high-level brain activation was observed in both ability groups when speaking L2. The high level of activation in people with high L2 ability when speaking either L2 or L1 suggested strong inhibition of the non-spoken language. A wider area of brain activation in people with low compared with high L2 ability when speaking L2 is considered to be attributed to the cognitive load involved in code-switching L1 to L2 with strong inhibition of L1 and the cognitive load involved in using L2.

scholarly journals Understanding Mental Health and Cognitive Restructuring With Ecological Neuroscience

2021 ◽  
Vol 12 ◽  
Author(s):  
James Crum
Keyword(s):  
Mental Health ◽  
Near Infrared ◽  
Ecological Validity ◽  
Experimental Designs ◽  
Functional Near Infrared Spectroscopy ◽  
Practical Applications ◽  
Recent Developments ◽  
The Brain ◽  
Neuroimaging Techniques ◽  
Selection Of

Neuroimaging and neuropsychological methods have contributed much toward an understanding of the information processing systems of the human brain in the last few decades, but to what extent do cognitive neuroscientific findings represent and generalize to the inter- and intra-brain dynamics engaged in adapting to naturalistic situations? If it is not marked, and experimental designs lack ecological validity, then this stands to potentially impact the practical applications of a paradigm. In no other domain is this more important to acknowledge than in human clinical neuroimaging research, wherein reduced ecological validity could mean a loss in clinical utility. One way to improve the generalizability and representativeness of findings is to adopt a more “real-world” approach to the development and selection of experimental designs and neuroimaging techniques to investigate the clinically-relevant phenomena of interest. For example, some relatively recent developments to neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) make it possible to create experimental designs using naturalistic tasks that would otherwise not be possible within the confines of a conventional laboratory. Mental health, cognitive interventions, and the present challenges to investigating the brain during treatment are discussed, as well as how the ecological use of fNIRS might be helpful in bridging the explanatory gaps to understanding the cultivation of mental health.

scholarly journals Functional Near Infrared Spectroscopy (fNIRS) based Odor Detection and Classification using Functional Data Analysis

2021 ◽  
Author(s):  
Faezeh Moradi ◽  
Shima T. Moein ◽  
Issa Zakeri ◽  
Kambiz Pourrezaei
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Near Infrared ◽  
Stimulus Condition ◽  
Functional Near Infrared Spectroscopy ◽  
Odor Detection ◽  
The Brain

AbstractAn objective approach for odor detection is to analyze the brain activity using imaging techniques during the odor stimulation. In this study, Functional Near Infrared Spectroscopy (fNIRS) is used to record hemodynamic response from the frontal region of the brain by using a 4-channel fNIRS system. The fNIRs data is collected during the odor detection task in which the subjects were asked to press a button when they detect the given odor. Functional Data Analysis (FDA) was applied on fNIRs data to convert discrete measured samples of data to continuous smooth curves. The FDA method enables us to use the bases coefficients of fNIRS smoothed curves for features that represent the shape of the raw fNIRS signal. With the learning algorithm that we proposed, these features were used to train the support vector machine classifier. We evaluated the odor detection problem, in two binary classification cases: odorant vs. non-odorant and odorant vs. fingertapping. The model achieved a classification accuracy of 94.12% and 97.06% over the stimulus condition in the two cases, respectively. Moreover to find the actual predictors we used the extracted defined features (slope, standard deviation, and delta) to train our classifier. We achieved an average accuracy of 91.18 % on classifying odorant vs. non-odorant and an accuracy of 94.12% for odorant vs. fingertapping on the stimulus condition. The results determined that fNIRs signals of odorant and non-odorant are distinguishable without being affected by the motor activity during the experiment.These findings suggest that fNIRs measurement on the forehead could be potentially used for objective and comparably inexpensive assessment of odor detection in cases that the subjective report is unreliable.

scholarly journals Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 389
Author(s):  
Kogulan Paulmurugan ◽  
Vimalan Vijayaragavan ◽  
Sayantan Ghosh ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Brain Function ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Neuron Function ◽  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

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