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

Exploring the physiological response differences of β-caryophyllene, linalool and citral inhalation and their anxiolytic potential

Essential oils with β-caryophyllene, citral, and linalool as key compounds often exhibit some anti-anxiety like effects in aromatherapy. However, evidence of the effect of these three compounds through human inhalation remains limited. It is worth exploring their potential anxiolytic effect through the olfactory pathway, and finding out whether the three compounds lead to different physiological responses. A total of 48 subjects were randomly assigned to three odor (β-caryophyllene, citral, and linalool) inhalation groups and one control (odorless jojoba oil) group. Stress stimulation was induced using n-back and mental arithmetic tasks. The odor was administered before the task test session. Assessments including the State-Trait Anxiety Inventory (STAI), electroencephalogram (EEG) activities, facial expressions, several physiological indicators, and a self-report scale of subjective perception of the odor environments were carried out. The changes before and after inhalation, as well as the inter-group differences, were analyzed. Both β-caryophyllene and citral inhalation led to a significant decrease in anxiety levels, while only β-caryophyllene resulted in a notable reduction across both sub-scales of STAI. Following the odor inhalation, heart rate significantly decreased in all three groups, with the β-caryophyllene group exhibiting the most pronounced decline. While the systolic blood pressure of the linalool group demonstrated a statistically significant difference. Regarding facial expressions, β-caryophyllene significantly increased the ratio of 'Happiness' and decreased the ratio of 'Fear'. In the non-task state, citral reduced the power of frontal alpha, delta, and theta waves while β-caryophyllene had a similar effect. All odor inhalation groups showed increased delta and theta waves after the task compared with the control group, with the β-caryophyllene group having notably lower frontal beta waves. β-Caryophyllene and citral exhibited good anti-anxiety effects. Subjects receiving different odors showed different EEG and physiological responses, indicating the differences in emotional regulation ways among the three compounds.

Decoding working-memory load duringn-back task performance from high channel fNIRS data

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

Investigating the relationship between CSAT scores and prefrontal fNIRS signals during cognitive tasks using a quantum annealing algorithm

Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Herein, we explored the association between cognitive tasks and academic achievement by analyzing prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection algorithm was applied to fNIRS data to identify cognitive tasks correlated with CSAT scores. Twelve features (signal mean, median, variance, peak, number of peaks, sum of peaks, range, minimum, kurtosis, skewness, standard deviation, and root mean square) were extracted from fNIRS signals at two time windows (10- and 60-s) to compare results from various feature variable conditions. The feature selection results from the QA-based and XGBoost regressor algorithms were compared to validate the former's performance. In a two-step validation process using multiple linear regression models, model fitness (adjusted R2) and model prediction error (RMSE) values were calculated. The quantum annealer demonstrated comparable performance to classical machine learning models, and specific cognitive tasks, including verbal fluency, recognition, and the Corsi block tapping task, were correlated with academic achievement. Group analyses revealed stronger associations between Tower of London and N-back tasks with higher CSAT scores. Quantum annealing algorithms have significant potential in feature selection using fNIRS data, and represents a novel research approach. Future studies should explore predictors of academic achievement and cognitive ability.

Cardiac vagal activity changes moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex

Phasic cardiac vagal activity (CVA), reflecting ongoing, moment-to-moment psychophysiological adaptations to environmental changes, can serve as a predictor of individual difference in executive function, particularly executive performance. However, the relationship between phasic CVA and executive function demands requires further validation because of previous inconsistent findings. Moreover, it remains unclear what types of phasic changes of CVA may be adaptive in response to heightened executive demands. This study used the standard N-back task to induce different levels of working memory (WM) load and combined functional Near-Infrared Spectroscopy (fNIRS) with a multipurpose polygraph to investigate the variations of CVA and its interactions with cognitive and prefrontal responses as executive demands increased in fifty-two healthy young subjects. Our results showed phasic decreases in CVA as WM load increased (t (51) = -3.758, p < 0.001, Cohen's d = 0.526). Furthermore, phasic changes of CVA elicited by increased executive demands moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex (B = 0.038, SE = 0.014, p < 0.05). Specifically, as executive demands increased, individuals with larger phasic CVA withdrawal showed a positive relationship between cognitive and hemodynamic variations in the prefrontal cortex (β = 0.281, p = 0.031). No such significant relationship was observed in individuals with smaller phasic CVA withdrawal. The current findings demonstrate a decrease in CVA with increasing executive demands and provide empirical support for the notion that a larger phasic CVA withdrawal can be considered adaptive in situations requiring high executive function demands.

Cognitive workload estimation using physiological measures: a review

Estimating cognitive workload levels is an emerging research topic in the cognitive neuroscience domain, as participants' performance is highly influenced by cognitive overload or underload results. Different physiological measures such as Electroencephalography (EEG), Functional Magnetic Resonance Imaging, Functional near-infrared spectroscopy, respiratory activity, and eye activity are efficiently used to estimate workload levels with the help of machine learning or deep learning techniques. Some reviews focus only on EEG-based workload estimation using machine learning classifiers or multimodal fusion of different physiological measures for workload estimation. However, a detailed analysis of all physiological measures for estimating cognitive workload levels still needs to be discovered. Thus, this survey highlights the in-depth analysis of all the physiological measures for assessing cognitive workload. This survey emphasizes the basics of cognitive workload, open-access datasets, the experimental paradigm of cognitive tasks, and different measures for estimating workload levels. Lastly, we emphasize the significant findings from this review and identify the open challenges. In addition, we also specify future scopes for researchers to overcome those challenges.

Interaction between visual working memory and upright postural control in young adults: an event-related potential study based on the n-back paradigm

As a part of the overall information-processing system of the brain, postural control is related to the cognitive processes of working memory. Previous studies have suggested that cognitive tasks and postural control processes can compete for resources in common brain areas, although there is an "inverted U" relationship between arousal level and behavioral control - the arousal level of individuals changes when performing cognitive tasks. However, the exact neural connections between the two are unclear. This may be related to the nature of cognitive tasks. Some studies believe that posture occupies not only spatial information processing resources but also visual non-spatial information processing resources. Other studies believe that posture control only occupies spatial information processing resources in the central system, but does not occupy non-spatial information processing resources. Previous studies used different cognitive task materials and reached different conclusions. In this study, we used the same visuospatial and non-spatial materials, the n-back visual working memory paradigm, the event-related potential technique to investigate the effects of visuospatial and non-spatial working memory tasks on adolescents' postural control under different cognitive loads. The results of this study showed that in both visuospatial and non-spatial conditions, the N1 effect of the parieto-occipital lobe was larger during upright posture than in the sitting position (160-180 ms), the P300 effect of the central parieto-occipital region (280-460 ms) was induced by working memory in different postures, and the P300 wave amplitude was higher in the sitting position than in the upright position. We demonstrated that upright postural control enhances early selective attention but interferes with central memory encoding, thus confirming that postural control and visuospatial and non-spatial working memory share brain regions and compete with each other.

A multimodal dataset for investigating working memory in presence of music: a pilot study

Introduction

Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli.

Methods

Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session.

Results

A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session.

Discussion

Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.

Evaluating Speaker-Listener Cognitive Effort in Speech Communication Through Brain-to-Brain Synchrony: A Pilot Functional Near-Infrared Spectroscopy Investigation

PURPOSE

We explore a new approach to the study of cognitive effort involved in listening to speech by measuring the brain activity in a listener in relation to the brain activity in a speaker. We hypothesize that the strength of this brain-to-brain synchrony (coupling) reflects the magnitude of cognitive effort involved in verbal communication and includes both listening effort and speaking effort. We investigate whether interbrain synchrony is greater in native-to-native versus native-to-nonnative communication using functional near-infrared spectroscopy (fNIRS).

METHOD

Two speakers participated, a native speaker of American English and a native speaker of Korean who spoke English as a second language. Each speaker was fitted with the fNIRS cap and told short stories. The native English speaker provided the English narratives, and the Korean speaker provided both the nonnative (accented) English and Korean narratives. In separate sessions, fNIRS data were obtained from seven English monolingual participants ages 20-24 years who listened to each speaker's stories. After listening to each story in native and nonnative English, they retold the content, and their transcripts and audio recordings were analyzed for comprehension and discourse fluency, measured in the number of hesitations and articulation rate. No story retellings were obtained for narratives in Korean (an incomprehensible language for English listeners). Utilizing fNIRS technique termed sequential scanning, we quantified the brain-to-brain synchronization in each speaker-listener dyad.

RESULTS

For native-to-native dyads, multiple brain regions associated with various linguistic and executive functions were activated. There was a weaker coupling for native-to-nonnative dyads, and only the brain regions associated with higher order cognitive processes and functions were synchronized. All listeners understood the content of all stories, but they hesitated significantly more when retelling stories told in accented English. The nonnative speaker hesitated significantly more often than the native speaker and had a significantly slower articulation rate. There was no brain-to-brain coupling during listening to Korean, indicating a break in communication when listeners failed to comprehend the speaker.

CONCLUSIONS

We found that effortful speech processing decreased interbrain synchrony and delayed comprehension processes. The obtained brain-based and behavioral patterns are consistent with our proposal that cognitive effort in verbal communication pertains to both the listener and the speaker and that brain-to-brain synchrony can be an indicator of differences in their cumulative communicative effort.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25452142.

Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks

Introduction

The efficiency and safety of complex high precision human-machine systems such as in aerospace and robotic surgery are closely related to the cognitive readiness, ability to manage workload, and situational awareness of their operators. Accurate assessment of mental workload could help in preventing operator error and allow for pertinent intervention by predicting performance declines that can arise from either work overload or under stimulation. Neuroergonomic approaches based on measures of human body and brain activity collectively can provide sensitive and reliable assessment of human mental workload in complex training and work environments.

Methods

In this study, we developed a new six-cognitive-domain task protocol, coupling it with six biomedical monitoring modalities to concurrently capture performance and cognitive workload correlates across a longitudinal multi-day investigation. Utilizing two distinct modalities for each aspect of cardiac activity (ECG and PPG), ocular activity (EOG and eye-tracking), and brain activity (EEG and fNIRS), 23 participants engaged in four sessions over 4 weeks, performing tasks associated with working memory, vigilance, risk assessment, shifting attention, situation awareness, and inhibitory control.

Results

The results revealed varying levels of sensitivity to workload within each modality. While certain measures exhibited consistency across tasks, neuroimaging modalities, in particular, unveiled meaningful differences between task conditions and cognitive domains.

Discussion

This is the first comprehensive comparison of these six brain-body measures across multiple days and cognitive domains. The findings underscore the potential of wearable brain and body sensing methods for evaluating mental workload. Such comprehensive neuroergonomic assessment can inform development of next generation neuroadaptive interfaces and training approaches for more efficient human-machine interaction and operator skill acquisition.

Brain activation during the N-back working memory task in individuals with spinal cord injury: a functional near-infrared spectroscopy study

Cognitive impairments have frequently been reported in individuals with spinal cord injury (SCI) across different domains such as working memory, attention, and executive function. The mechanism of cognitive impairment after SCI is not well understood due to the heterogeneity of SCI sample populations, and may possibly be due to factors such as cardiovascular dysfunction, concomitant traumatic brain injury (TBI), hypoxia, sleep disorders, and body temperature dysregulation. In this study, we implement the Neuropsychiatric Unit Cognitive Assessment Tool (NUCOG) to assess cognitive differences between individuals with SCI and age-matched able-bodied (AB) controls. We then use an N-back working memory task and functional near-infrared spectroscopy (fNIRS) to elucidate the neurovascular correlates of cognitive function in individuals with SCI. We observed significant differences between the SCI and AB groups on measures of executive function on the NUCOG test. On the N-back task, across the three levels of difficulty: 0-back, 2-back, and 3-back, no significant differences were observed between the SCI and AB group; however, both groups performed worse as the level of difficulty increased. Although there were no significant differences in N-back performance scores between the two groups, functional brain hemodynamic activity differences were observed between the SCI and AB groups, with the SCI group exhibiting higher maximum oxygenated hemoglobin concentration in the right inferior parietal lobe. These findings support the use of fNIRS to study cognitive function in individuals with SCI and may provide a useful tool during rehabilitation to obtain quantitative functional brain activity metrics.

Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli

Emotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent -deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task.

Wearable functional near-infrared spectroscopy for measuring dissociable activation dynamics of prefrontal cortex subregions during working memory

The prefrontal cortex (PFC) has been extensively studied in relation to various cognitive abilities, including executive function, attention, and memory. Nevertheless, there is a gap in our scientific knowledge regarding the functionally dissociable neural dynamics across the PFC during a cognitive task and their individual differences in performance. Here, we explored this possibility using a delayed match-to-sample (DMTS) working memory (WM) task using NIRSIT, a high-density, wireless, wearable functional near-infrared spectroscopy (fNIRS) system. First, upon presentation of the sample stimulus, we observed an immediate signal increase in the ventral (orbitofrontal) region of the anterior PFC, followed by activity in the dorsolateral PFC. After the DMTS test stimulus appeared, the orbitofrontal cortex activated once again, while the rest of the PFC showed overall disengagement. Individuals with higher accuracy showed earlier and sustained activation of the PFC across the trial. Furthermore, higher network efficiency and functional connectivity in the PFC were correlated with individual WM performance. Our study sheds new light on the dynamics of PFC subregional activity during a cognitive task and its potential applicability in explaining individual differences in experimental, educational, or clinical populations. PRACTITIONER POINTS: Wearable functional near-infrared spectroscopy (fNIRS) captured dissociable temporal dynamics across prefrontal subregions during a delayed match-to-sample task. Anterior regions of the orbitofrontal cortex (OFC) activated first during the delay period, followed by the dorsolateral prefrontal cortex (PFC). PFC disengaged overall after the delay, but the OFC reactivated to the test stimulus. Earlier and sustained activation of PFC was associated with better accuracy. Functional connectivity and network efficiency also varied with task performance.

A comprehensive research setup for monitoring Alzheimer's disease using EEG, fNIRS, and Gait analysis

Alzheimer's disease (AD) has a detrimental impact on brain function, affecting various aspects such as cognition, memory, language, and motor skills. Previous research has dominantly used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to individually measure brain signals or combine the two methods to target specific brain functions. However, comprehending Alzheimer's disease requires monitoring various brain functions rather than focusing on a single function. This paper presents a comprehensive research setup for a monitoring platform for AD. The platform incorporates a 32-channel dry electrode EEG, a custom-built four-channel fNIRS, and gait monitoring using a depth camera and pressure sensor. Various tasks are employed to target multiple brain functions. The paper introduced the detailed instrumentation of the fNIRS system, which measures the prefrontal cortex, outlines the experimental design targeting various brain functioning programmed in BCI2000 for visualizing EEG signals synchronized with experimental stimulation, and describes the gait monitoring hardware and software and protocol design. The ultimate goal of this platform is to develop an easy-to-perform brain and gait monitoring method for elderly individuals and patients with Alzheimer's disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00306-7.

Validation of a mobile fNIRS device for measuring working memory load in the prefrontal cortex

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that measures cortical blood flow to infer neural activation. Traditionally limited to laboratory settings due to high costs and complex operation, recent advancements have introduced mobile fNIRS devices, significantly broadening the scope of potential research participants. This study validates the use of the Mendi, a two-channel mobile fNIRS system, for measuring prefrontal oxyhemoglobin concentration changes during an n-back task. We manipulated task difficulty through different n-back levels (one-back versus three-back), revealing increased oxyhemoglobin concentrations in the prefrontal cortex during the more demanding three-back task compared to the one-back task. This finding demonstrates the Mendi's ability to distinguish between low and high cognitive task loads. Behavioural data, showing a decrease in accuracy under high load conditions, further corroborates these neuroimaging findings. Our study validates the Mendi mobile fNIRS system as an effective tool for assessing working memory load and underscores its potential in enhancing neuroscientific research accessibility. The user-friendly and cost-effective nature of mobile fNIRS systems like the Mendi opens up neuroscientific research to a diverse set of participants, enabling the investigation of neural processes in real-world environments across a variety of demographic groups.

Mental workload classification using convolutional neural networks based on fNIRS-derived prefrontal activity

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) is a tool to assess brain activity during cognitive testing. Despite its usefulness, its feasibility in assessing mental workload remains unclear. This study was to investigate the potential use of convolutional neural networks (CNNs) based on functional near-infrared spectroscopy (fNIRS)-derived signals to classify mental workload in individuals with mild cognitive impairment.

METHODS

Spatial images by constructing a statistical activation map from the prefrontal activity of 120 subjects with MCI performing three difficulty levels of the N-back task (0, 1, and 2-back) were used for CNNs. The CNNs were evaluated using a 5 and 10-fold cross-validation method.

RESULTS

As the difficulty level of the N-back task increased, the accuracy decreased and prefrontal activity increased. In addition, there was a significant difference in the accuracy and prefrontal activity across the three levels (p's < 0.05). The accuracy of the CNNs based on fNIRS-derived spatial images evaluated by 5 and 10-fold cross-validation in classifying the difficulty levels ranged from 0.83 to 0.96.

CONCLUSION

fNIRS could also be a promising tool for measuring mental workload in older adults with MCI despite their cognitive decline. In addition, this study demonstrated the feasibility of the classification performance of the CNNs based on fNIRS-derived signals from the prefrontal cortex.

Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome

Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.

Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings

Collaborative robots (cobots) have largely replaced conventional industrial robots in today's workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker's performance in a human-robot collaborative environment. In this study, factory workers' mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

From brain to worksite: the role of fNIRS in cognitive studies and worker safety

Effective hazard recognition and decision-making are crucial factors in ensuring workplace safety in the construction industry. Workers' cognition closely relates to that hazard-handling behavior. Functional near-infrared spectroscopy (fNIRS) is a neurotechique tool that can evaluate the concentration vibration of oxygenated hemoglobin [ H b O 2 ] and deoxygenated hemoglobin [H b R ] to reflect the cognition process. It is essential to monitor workers' brain activity by fNIRS to analyze their cognitive status and reveal the mechanism in hazard recognition and decision-making process, providing guidance for capability evaluation and management enhancement. This review offers a systematic assessment of fNIRS, encompassing the basic theory, experiment analysis, data analysis, and discussion. A literature search and content analysis are conducted to identify the application of fNIRS in construction safety research, the limitations of selected studies, and the prospects of fNIRS in future research. This article serves as a guide for researchers keen on harnessing fNIRS to bolster construction safety standards and forwards insightful recommendations for subsequent studies.

Functional near-infrared spectroscopy-based prefrontal cortex oxygenation during working memory tasks in sickle cell disease

Significance

Sickle cell disease (SCD), characterized by painful vaso-occlusive crises, is associated with cognitive decline. However, objective quantification of cognitive decline in SCD remains a challenge, and the associated hemodynamics are unknown.

Aim

To address this, we utilized functional near-infrared spectroscopy (fNIRS) to measure prefrontal cortex (PFC) oxygenation responses to N -back working memory tasks in SCD patients and compared them with healthy controls.

Approach

We quantified the PFC oxygenation rate as an index of cognitive activity in each group and compared them. In half of the participants, a Stroop test was administered before they started N -back to elevate their baseline stress level.

Results

In SCD compared to healthy controls, we found that (1) under a high baseline stress level, there were significantly greater oxygenation responses during the 2-back task, further elevated with histories of stroke; (2) there was a marginally slower N -back response time, and it was even slower with a history of stroke; and (3) the task accuracy was not different.

Conclusions

Additional requirements for processing time, PFC resources, and PFC oxygenation in SCD patients offer an important basis for understanding their cognitive decline and highlight the potential of fNIRS for evaluating cognitive functions.

Cognitive training based on functional near-infrared spectroscopy neurofeedback for the elderly with mild cognitive impairment: a preliminary study

Introduction

Mild cognitive impairment (MCI) is often described as an intermediate stage of the normal cognitive decline associated with aging and dementia. There is a growing interest in various non-pharmacological interventions for MCI to delay the onset and inhibit the progressive deterioration of daily life functions. Previous studies suggest that cognitive training (CT) contributes to the restoration of working memory and that the brain-computer-interface technique can be applied to elicit a more effective treatment response. However, these techniques have certain limitations. Thus, in this preliminary study, we applied the neurofeedback paradigm during CT to increase the working memory function of patients with MCI.

Methods

Near-infrared spectroscopy (NIRS) was used to provide neurofeedback by measuring the changes in oxygenated hemoglobin in the prefrontal cortex. Thirteen elderly MCI patients who received CT-neurofeedback sessions four times on the left dorsolateral prefrontal cortex (dlPFC) once a week were recruited as participants.

Results

Compared with pre-intervention, the activity of the targeted brain region increased when the participants first engaged in the training; after 4 weeks of training, oxygen saturation was significantly decreased in the left dlPFC. The participants demonstrated significantly improved working memory compared with pre-intervention and decreased activity significantly correlated with improved cognitive performance.

Conclusion

Our results suggest that the applications for evaluating brain-computer interfaces can aid in elucidation of the subjective mental workload that may create additional or decreased task workloads due to CT.

Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification

Humans' performance varies due to the mental resources that are available to successfully pursue a task. To monitor users' current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

A pilot study to explore the effect of udenafil on cerebral hemodynamics in older adults

OBJECTIVE

Phosphodiesterase-5 inhibitors (PDE5Is) enhance vasodilation. We investigated the effects of PDE5I on cerebral hemodynamics during cognitive tasks using functional near-infrared spectroscopy (fNIRS).

METHODS

This study used a crossover design. Twelve cognitively healthy men participants (mean age, 59 ± 3 years; range, 55-65 years) were recruited and randomly assigned to the experimental or control arm, then the experimental and control arm were exchanged after 1 week. Udenafil 100 mg was administered to participants in the experimental arm once daily for 3 days. We measured the fNIRS signal during the resting state and four cognitive tasks three times for each participant: at baseline, in the experimental arm, and in the control arm.

RESULTS

Behavioral data did not show a significant difference between the experimental and control arms. The fNIRS signal showed significant decreases in the experimental arm compared to the control arm during several cognitive tests: verbal fluency test (left dorsolateral prefrontal cortex, T = -3.02, p = 0.014; left frontopolar cortex, T = -4.37, p = 0.002; right dorsolateral prefrontal cortex, T = -2.59, p = 0.027), Korean-color word Stroop test (left orbitofrontal cortex, T = -3.61, p = 0.009), and social event memory test (left dorsolateral prefrontal cortex, T = -2.35, p = 0.043; left frontopolar cortex, T = -3.35, p = 0.01).

INTERPRETATION

Our results showed a paradoxical effect of udenafil on cerebral hemodynamics in older adults. This contradicts our hypothesis, but it suggests that fNIRS is sensitive to changes in cerebral hemodynamics in response to PDE5Is.

Functional near-infrared spectroscopy neurofeedback of dorsolateral prefrontal cortex enhances human spatial working memory

Significance

Spatial working memory (SWM) is essential for daily life and deficits in this domain represent a common impairment across aging and several mental disorders. Impaired SWM has been closely linked to dysregulations in dorsolateral prefrontal cortex (DLPFC) activation.

Aim

The present study evaluates the feasibility and maintenance of functional near-infrared spectroscopy neurofeedback (fNIRS-NF) training of the DLPFC to enhance SWM in healthy individuals using a real-time fNIRS-NF platform developed by the authors.

Approach

We used a randomized sham-controlled between-subject fNIRS-NF design with 60 healthy subjects as a sample. Training-induced changes in the DLPFC, SWM, and attention performance served as primary outcomes.

Results

Feedback from the target channel significantly increased regional-specific DLPFC activation over the fNIRS-NF training compared to sham NF. A significant group difference in NF-induced frontoparietal connectivity was observed. Compared to the control group, the experimental group demonstrated significantly improved SWM and attention performance that were maintained for 1 week. Furthermore, a mediation analysis demonstrated that increased DLPFC activation mediated the effects of fNIRS-NF treatment on better SWM performance.

Conclusions

The present results demonstrated that successful self-regulation of DLPFC activation may represent a long-lasting intervention to improve human SWM and has the potential for further applications.

Benchmarking framework for machine learning classification from fNIRS data

Background

While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces.

Methods

We present an open-source benchmarking framework, BenchNIRS, to establish a best practice machine learning methodology to evaluate models applied to fNIRS data, using five open access datasets for brain-computer interface (BCI) applications. The BenchNIRS framework, using a robust methodology with nested cross-validation, enables researchers to optimise models and evaluate them without bias. The framework also enables us to produce useful metrics and figures to detail the performance of new models for comparison. To demonstrate the utility of the framework, we present a benchmarking of six baseline models [linear discriminant analysis (LDA), support-vector machine (SVM), k-nearest neighbours (kNN), artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM)] on the five datasets and investigate the influence of different factors on the classification performance, including: number of training examples and size of the time window of each fNIRS sample used for classification. We also present results with a sliding window as opposed to simple classification of epochs, and with a personalised approach (within subject data classification) as opposed to a generalised approach (unseen subject data classification).

Results and discussion

Results show that the performance is typically lower than the scores often reported in literature, and without great differences between models, highlighting that predicting unseen data remains a difficult task. Our benchmarking framework provides future authors, who are achieving significant high classification scores, with a tool to demonstrate the advances in a comparable way. To complement our framework, we contribute a set of recommendations for methodology decisions and writing papers, when applying machine learning to fNIRS data.

Changes in task performance and frontal cortex activation within and over sessions during the n-back task

The n-back task is a popular paradigm for studying neurocognitive processing at varying working memory loads. Although much is known about the effects of load on behavior and neural activation during n-back performance, the temporal dynamics of such effects remain unclear. Here, we investigated the within- and between-session stability and consistency of task performance and frontal cortical activation during the n-back task using functional near-infrared spectroscopy (fNIRS). Forty healthy young adults performed the 1-back and 3-back conditions three times per condition. They then undertook identical retest sessions 3 weeks later (M = 21.2 days, SD = 0.9). Over the course of the task, activation in the participants' frontopolar, dorsomedial, dorsolateral, ventrolateral, and posterolateral frontal cortices was measured with fNIRS. We found significantly improved working memory performance (difference between 1-back and 3-back accuracies) over time both within and between sessions. All accuracy and reaction time measures exhibited good to excellent consistency within and across sessions. Additionally, changes in frontal oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration were maintained over time across timescales, except that load-dependent (3-back > 1-back) HbO changes, particularly in the ventrolateral PFC, diminished over separate sessions. The consistency of fNIRS measures varied greatly, with changes in 3-back dorsolateral and ventrolateral HbO demonstrating fair-to-good consistency both within and between sessions. Overall, this study clarified the temporal dynamics of task performance and frontal activation during the n-back task. The findings revealed the neural mechanisms underlying the change in n-back task performance over time and have practical implications for future n-back research.

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

Background

Both electromagnetic radiation (EMR) and low-frequency noise (LFN) are widespread and influential environmental factors, and operators are inevitably exposed to both EMR and LFN within a complex exposure environment. The potential adverse effects of such exposure on human health must be considered seriously. This study aimed to investigate the effects of EMR and LFN on cognitive function as well as their interaction effect, which remain unclear.

Methods

Sixty young male college students were randomly grouped and experiments were conducted with a 2 × 2 factorial design in a shielded chamber. Mental workload (MWL) levels of the study subjects were measured and assessed using the NASA-task load index (TLX) subjective scale, an n-back task paradigm, and the functional near-infrared spectroscopy (fNIRS) imaging technique.

Results

For the 3-back task, the NASA-TLX subjective scale revealed a statistically significant main effect of LFN intensity, which enhanced the subjects' MWL level (F = 8.716, p < 0.01). Behavioral performance revealed that EMR intensity (430.1357 MHz, 10.75 W/m²) and LFN intensity (0-200 Hz, 72.9 dB) had a synergistic interaction effect, and the correct response time was statistically significantly prolonged by the combined exposure (F = 4.343, p < 0.05). The fNIRS imaging technique revealed a synergistic interaction effect between operational EMR intensity and operational LFN intensity, with statistically significant effects on the activation levels in the left and right dorsolateral prefrontal cortex (DLPFC). The mean β values of DLPFC were significantly increased (L-DLPFC F = 5.391, p < 0.05, R-DLPFC F = 4.222, p < 0.05), and the relative concentrations of oxyhemoglobin in the DLPFC were also significantly increased (L-DLPFC F = 4.925, p < 0.05, R-DLPFC F = 9.715, p < 0.01).

Conclusion

We found a statistically significant interaction effect between EMR (430.1357 MHz, 10.75 W/m²) and LFN (0-200 Hz, 72.9 dB) when simultaneously exposing subjects to both for 30 min. We conclude that exposure to this complex environment can cause a statistically significant increase in the MWL level of operators, and even alterations in their cognitive function.

Hotspots and trends in fNIRS disease research: A bibliometric analysis

Objective

To summarize the general information and hotspots of functional near-infrared spectroscopy (fNIRS)-based clinical disease research over the past 10 years and provide some references for future research.

Methods

The related literature published between 1 January 2011 and 31 January 2022 was retrieved from the Web of Science core database (WoS). Bibliometric visualization analysis of countries/regions, institutions, authors, journals, keywords and references were conducted by using CiteSpace 6.1.R3.

Results

A total of 467 articles were included, and the annual number of articles published over nearly a decade showed an upward trend year-by-year. These articles mainly come from 39 countries/regions and 280 institutions. The representative country and institution were the USA and the University of Tubingen. We identified 266 authors, among which Andreas J Fallgatter and Ann-Christine Ehlis were the influential authors. Neuroimage was the most co-cited journal. The major topics in fNIRS disease research included activation, prefrontal cortex, working memory, cortex, and functional magnetic resonance imaging (fMRI). In recent years, the Frontier topics were executive function, functional connectivity, performance, diagnosis, Alzheimer's disease, children, and adolescents. Based on the burst of co-cited references, gait research has received much attention.

Conclusion

This study conducted a comprehensive, objective, and visual analysis of publications, and revealed the status of relevant studies, hot topics, and trends concerning fNIRS disease research from 2011 to 2022. It is hoped that this work would help researchers to identify new perspectives on potential collaborators, important topics, and research Frontiers.

Nested and robust modeling techniques for fNIRS data with demographics and experiment related factors in n-back task

Functional near-infrared spectroscopy (fNIRS) signals are used to measure relative changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations. Brain response studies constitute multilevel or nested datasets formed by different parts of the brain of individuals and multidimensional datasets. The changes in brain activities under specific stimuli are investigated with the help of statistical analysis. However, these studies ignore the dependence structure between the repeated measures of the same subject, which may cause inaccurate or incomplete findings. In this study, we adopt an advanced statistical method into HbO data controlling for variability within repeated measures of each subject while testing and measuring the degrees of the statistical significance between-subject factors and explanatory variables. The changes in HbO are investigated through a linear mixed model, taking experimental and demographic variables into account with open access neuroscience data. The channels nested within subjects are considered random to capture the differences among individuals. Our findings reveal that n-back conditions and mean response times of the subjects have statistically significant associations with mean HbO.

Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory

Although theoretical studies have suggested that working-memory capacity is crucial for academic achievement, few empirical studies have directly investigated the relationship between working-memory capacity and programming ability, and no direct neural evidence has been reported to support this relationship. The present study aimed to fill this gap in the literature. Using a between-subject design, 17 programming novices and 18 advanced students performed an n-back working-memory task. During the experiment, their prefrontal hemodynamic responses were measured using a 48-channel functional near-infrared spectroscopy (fNIRS) device. The results indicated that the advanced students had a higher working-memory capacity than the novice students, validating the relationship between programming ability and working memory. The analysis results also showed that the hemodynamic responses in the prefrontal cortex can be used to discriminate between novices and advanced students. Additionally, we utilized an attention-based convolutional neural network to analyze the spatial domains of the fNIRS signals and demonstrated that the left prefrontal cortex was more important than other brain regions for programming ability prediction. This result was consistent with the results of statistical analysis, which in turn improved the interpretability of neural networks.

Load-Dependent Prefrontal Cortex Activation Assessed by Continuous-Wave Near-Infrared Spectroscopy during Two Executive Tasks with Three Cognitive Loads in Young Adults

The present study examined the evolution of the behavioral performance, subjectively perceived difficulty, and hemodynamic activity of the prefrontal cortex as a function of cognitive load during two different cognitive tasks tapping executive functions. Additionally, it investigated the relationships between these behavioral, subjective, and neuroimaging data. Nineteen right-handed young adults (18–22 years) were scanned using continuous-wave functional near-infrared spectroscopy during the performance of n-back and random number generation tasks in three cognitive load conditions. Four emitter and four receptor optodes were fixed bilaterally over the ventrolateral and dorsolateral prefrontal cortices to record the hemodynamic changes. A self-reported scale measured the perceived difficulty. The findings of this study showed that an increasing cognitive load deteriorated the behavioral performance and increased the perceived difficulty. The hemodynamic activity increased parametrically for the three cognitive loads of the random number generation task and in a two-back and three-back compared to a one-back condition. In addition, the hemodynamic activity was specifically greater in the ventrolateral prefrontal cortex than in the dorsolateral prefrontal cortex for both cognitive tasks (random number generation and n-back tasks). Finally, the results highlighted some links between cerebral oxygenation and the behavioral performance, but not the subjectively perceived difficulty. Our results suggest that cognitive load affects the executive performance and perceived difficulty and that fNIRS can be used to specify the prefrontal cortex’s implications for executive tasks involving inhibition and working memory updating.

Effects of Personalized Cognitive Training with the Machine Learning Algorithm on Neural Efficiency in Healthy Younger Adults

To date, neural efficiency, an ability to economically utilize mental resources, has not been investigated after cognitive training. The purpose of this study was to provide customized cognitive training and confirm its effect on neural efficiency by investigating prefrontal cortex (PFC) activity using functional near-infrared spectroscopy (fNIRS). Before training, a prediction algorithm based on the PFC activity with logistic regression was used to predict the customized difficulty level with 86% accuracy by collecting data when subjects performed four kinds of cognitive tasks. In the next step, the intervention study was designed using one pre-posttest group. Thirteen healthy adults participated in the virtual reality (VR)-based spatial cognitive training, which was conducted four times a week for 30 min for three weeks with customized difficulty levels for each session. To measure its effect, the trail-making test (TMT) and hemodynamic responses were measured for executive function and PFC activity. During the training, VR-based spatial cognitive performance was improved, and hemodynamic values were gradually increased as the training sessions progressed. In addition, after the training, the performance on the trail-making task (TMT) demonstrated a statistically significant improvement, and there was a statistically significant decrease in the PFC activity. The improved performance on the TMT coupled with the decreased PFC activity could be regarded as training-induced neural efficiency. These results suggested that personalized cognitive training could be effective in improving executive function and neural efficiency.

EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning

There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG-functional near-infrared spectroscopy (EEG-fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5-4 Hz), theta (4-7 Hz) and alpha (8-15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8).

Is there a relationship between cardiorespiratory fitness and executive performance as function of mental workload in young adults?

In the current study, we have decided to investigate the relationship between cardiorespiratory fitness and executive functions in young adults as a function of mental workload. To achieve our objectives, we have solicited 29 young adults (18–25 years; 12 women) who have first realized the Random Number Generation (RNG) task with two levels of complexity. After each level of complexity, the participants were asked to report on their perceived difficulty. Secondly, participants performed the RABIT® test, during which oxygen consumption was measured through the Metamax 3B-R2. The results showed that executive performance and perceived difficulty deteriorate with increasing task complexity. Additionally, oxygen consumption increased significantly to reach a peak during the hardest phase of the RABIT® test. Finally, as in previous studies, we could not observe a correlation between cardiorespiratory fitness and executive functions. Our findings support the lack of a direct relationship between cardiorespiratory fitness and executive functions. Future studies should explore the relationship between the composite measure of executive function, hemodynamic activity, and cardiorespiratory fitness in healthy youth and their peers with cardiovascular disease. This will examine an indirect effect of Cardiorespiratory fitness (CRF) on Executive functions (EFs) through brain activity.

The Effect of Copaiba Oil Odor on Anxiety Relief in Adults under Mental Workload: A Randomized Controlled Trial

Background

Aromatherapy has been proved to be effective in alleviating anxiety in practices and research. Recently, copaiba oil (CPO) is popular in the market and is recommended for anxiety relief in aromatherapy practice. However, relevant scientific research is still lacking.

Methods

A randomized controlled trial was designed to evaluate the anxiety-relieving effect of CPO inhalation in 22 adults. Jojoba oil was used as the control treatment. N-back and mental arithmetic tasks were used as stress stimulation. CPO or control intervention was carried out after the n-back training phase. The State-Trait Anxiety Inventory (STAI), EEG activities, physiological indexes including heart rate (HR), blood pressure (BP), blood oxygen saturation, and salivary cortisol were assessed in different phases of the experimental process.

Results

There was no significant difference in the change of HR and BP between the CPO and control groups before odor intervention. The S-AI scores of the CPO treated participants decreased after the n-back and mental arithmetic tests and were significantly lower than those of the participants who received control treatments. The HR and salivary cortisol of participants who received CPO intervention significantly decreased during the n-back and mental arithmetic tests. Furthermore, a remarkable decrease of beta wave activity was observed in the left midfrontal region (F3) when the participant received the CPO intervention.

Conclusion

The study's findings supported that the CPO odor showed beneficial effects on alleviating anxiety based on several indicators in subjective, physiological, and EEG measurements.

Recognition of Attentional States in VR Environment: An fNIRS Study

An improvement in ecological validity is one of the significant challenges for 21st-century neuroscience. At the same time, the study of neurocognitive processes in real-life situations requires good control of all variables relevant to the results. One possible solution that combines the capability of creating realistic experimental scenarios with adequate control of the test environment is virtual reality. Our goal was to develop an integrative research workspace involving a CW-fNIRS and head-mounted-display (HMD) technology dedicated to offline and online cognitive experiments. We designed an experimental study in a repeated-measures model on a group of BCI-naïve participants to verify our assumptions. The procedure included a 3D environment-adapted variant of the classic n-back task (2-back version). Tasks were divided into offline (calibration) and online (feedback) sessions. In both sessions, the signal was recorded during the cognitive task for within-group comparisons of changes in oxy-Hb concentration in the regions of interest (the dorsolateral prefrontal cortex-DLPFC and middle frontal gyrus-MFG). In the online session, the recorded signal changes were translated into real-time feedback. We hypothesized that it would be possible to obtain significantly higher than the level-of-chance threshold classification accuracy for the enhanced attention engagement (2-back task) vs. relaxed state in both conditions. Additionally, we measured participants' subjective experiences of the BCI control in terms of satisfaction. Our results confirmed hypotheses regarding the offline condition. In accordance with the hypotheses, combining fNIRS and HMD technologies enables the effective transfer of experimental cognitive procedures to a controlled VR environment. This opens the new possibility of creating more ecologically valid studies and training procedures.

Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights

Monitoring pilots' cognitive states becomes increasingly important in aviation. Physiological measurement can detect increased mental workload (MWL) even before performance declines. Yet, changes in MWL are rarely varied systematically and few studies control for confounding effects of other cognitive states. The present study targets these shortcomings by analysing the effects of stepwise increased MWL on cortical activation, while controlling for mental fatigue (MF). 35 participants conducted a simulated flight with an incorporated adapted n-back and monitoring task. We recorded cortical activation with concurrent EEG and fNIRS measurement, performance, self-reported MWL and MF. Our results show the successful manipulation of MWL without confounding effects of MF. Higher task difficulty elicited higher subjective MWL ratings, performance decline, higher frontal theta activity and reduced frontal deoxyhaemoglobin (Hbr) concentration. Using both EEG and fNIRS, we could discriminate all induced MWL levels. fNIRS was more sensitive to tasks with low difficulty, and EEG to tasks with high difficulty. Our findings further suggest a plateau effect for high MWL that could present an upper boundary to individual cognitive capacity. Our results highlight the benefits of physiological measurement in aviation, both for assessment of cognitive states and as a data source for adaptive assistance systems.

Functional near-infrared spectroscopy in the neuropsychological assessment of spatial memory: A systematic review

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical imaging technique that employs near-infrared light to measure cortical brain oxygenation. The use of fNIRS has increased exponentially in recent years. Spatial memory is defined as the ability to learn and use spatial information. This neuropsychological process is constantly used in our daily lives and can be measured by fNIRS but no research has reviewed whether this technique can be useful in the neuropsychological assessment of spatial memory. This study aimed to review empirical work on the use of fNIRS in the neuropsychological assessment of human spatial memory. We used four databases: PubMed, PsycINFO, Scopus and Web of Science, and a total of 18 articles were found to be eligible. Most of the articles assessed spatial or visuospatial working memory with a predominance in computer-based tasks, used fNIRS equipment of 16 channels and mainly measured the prefrontal cortex (PFC). The studies analysed found linear or quadratic relationships between working memory load and PFC activity, greater activation of PFC activity and worse behavioural results in healthy older people in comparison with healthy adults, and hyperactivation of PFC as a form of compensation in clinical samples. We conclude that fNIRS is compatible with the standard neuropsychological assessment of spatial memory, making it possible to complement behavioural results with data of cortical functional activity.

Attention Classification Based on Biosignals during Standard Cognitive Tasks for Occupational Domains

Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.

Functional Connectivity Analysis and Detection of Mental Fatigue Induced by Different Tasks Using Functional Near-Infrared Spectroscopy

Objectives

The objective of this study was to investigate common functional near-infrared spectroscopy (fNIRS) features of mental fatigue induced by different tasks. In addition to distinguishing fatigue from non-fatigue state, the early signs of fatigue were also studied so as to give an early warning of fatigue.

Methods

fNIRS data from 36 participants were used to investigate the common character of functional connectivity network corresponding to mental fatigue, which was induced by psychomotor vigilance test (PVT), cognitive work, or simulated driving. To analyze the network reorganizations quantitatively, clustering coefficient, characteristic path length, and small worldness were calculated in five sub-bands (0.6-2.0, 0.145-0.600, 0.052-0.145, 0.021-0.052, and 0.005-0.021 Hz). Moreover, we applied a random forest method to classify three fatigue states.

Results

In a moderate fatigue state: the functional connectivity strength between brain regions increased overall in 0.021-0.052 Hz, and an asymmetrical pattern of connectivity (right hemisphere > left hemisphere) was presented. In 0.052-0.145 Hz, the connectivity strength decreased overall, the clustering coefficient decreased, and the characteristic path length increased significantly. In severe fatigue state: in 0.021-0.052 Hz, the brain network began to deviate from a small-world pattern. The classification accuracy of fatigue and non-fatigue was 85.4%. The classification accuracy of moderate fatigue and severe fatigue was 82.8%.

Conclusion

The preliminary research demonstrates the feasibility of detecting mental fatigue induced by different tasks, by applying the functional network features of cerebral hemoglobin signal. This universal and robust method has the potential to detect early signs of mental fatigue and prevent relative human error in various working environments.

Inter-Brain Synchrony Levels According to Task Execution Modes and Difficulty Levels: an fNIRS/GSR Study

Hyperscanning is a brain imaging technique that measures brain synchrony caused by social interactions. Recent research on hyperscanning has revealed substantial inter-brain synchrony (IBS), but little is known about the link between IBS and mental workload. To study this link, we conducted an experiment consisting of button-pressing tasks of three different difficulty levels for the cooperation and competition modes with 56 participants aged 23.7±3.8 years (mean±standard deviation). We attempted to observe IBS using functional near-infrared spectroscopy (fNIRS) and galvanic skin response (GSR) to assess the activities of the human autonomic nervous system. We found that the IBS levels increased in a frequency band of 0.075-0.15 Hz, which was unrelated to the task repetition frequency in the cooperation mode according to the task difficulty level. Significant relative inter-brain synchrony (RIBS) increases were observed in three and 10 channels out of 15 for the hard tasks compared to the normal and easy tasks, respectively. We observed that the average GSR values increased with increasing task difficulty levels for the competition mode only. Thus, our results suggest that the IBS revealed by fNIRS and GSR is not related to the hemodynamic changes induced by mental workload, simple behavioral synchrony such as button-pressing timing, or autonomic nervous system activity. IBS is thus explicitly caused by social interactions such as cooperation.

Functional near-infrared spectroscopy as a potential objective evaluation technique in neurocognitive disorders after traumatic brain injury

Most patients with neurocognitive disorders after traumatic brain injury (TBI) show executive dysfunction, in which the pre-frontal cortex (PFC) plays an important role. However, less objective evaluation technique could be used to assess the executive dysfunction in these patients. Functional near-infrared spectroscopy (fNIRS), which is a non-invasive technique, has been widely used in the study of psychiatric disorders, cognitive dysfunction, etc. The present study aimed to explore whether fNIRS could be a technique to assess the damage degree of executive function in patients with neurocognitive disorders after TBI by using the Stroop and N-back tasks in PFC areas. We enrolled 37 patients with neurocognitive disorders after TBI and 60 healthy controls. A 22-channel fNIRS device was used to record HbO during Stroop, 1-back and 2-back tasks. The results showed that patients made significantly more errors and had longer response times than healthy controls. There were statistically significant differences in HbO level variation in bilateral frontopolar, bilateral inferior frontal gyrus and left middle temporal gyrus during Stroop color word consistency tasks and in left frontopolar during Stroop color word inconsistency tasks. During 2-back tasks, there were also statistically significant differences in HbO level variation in bilateral frontopolar, bilateral inferior frontal gyrus, bilateral dorsolateral pre-frontal cortex. According to brain activation maps, the patients exhibited lower but more widespread activation during the 2-back and Stroop color word consistency tasks. The fNIRS could identify executive dysfunction in patients with neurocognitive disorders after TBI by detecting HbO levels, which suggested that fNIRS could be a potential objective evaluation technique in neurocognitive disorders after TBI.

Fatigue, Stress, and Performance during Alternating Physical and Cognitive Tasks-Effects of the Temporal Pattern of Alternations

In occupational life, performing cognitive work tasks in between fatiguing physical work tasks may allow recovery and reduce stress without losing productive working time. The temporal pattern of such alternations is likely a determinant of the recovery effect, influencing both stress and fatigue; the difficulty of the cognitive task (CT) would also be a likely determinant. The aim of this study was to determine to what extent the temporal pattern of alternations between a repetitive physical task and a CT of different difficulties influenced perceived fatigability, performance fatigability, stress-related outcomes, and performance. Fifteen women performed four work sessions comprising 110 min of repeated bouts of a repetitive physical task (pipetting), alternating with a CT (n-back). Sessions differed in bout cycle time (short: 7 + 3 min versus long: 14 + 6 min) and CT difficulty (CTdiff; easy versus difficult). Fatigue was assessed from recordings of maximal voluntary contraction force in shoulder elevations and handgrip pre- and post-work, electromyography (EMG) from the right trapezius and right forearm extensors during work, and repeated self-ratings of fatigue and pain throughout the session. Stress was assessed using electrocardiography (heart rate variability), salivary alpha-amylase, and self-reports. Perceived fatigue increased significantly over time for all protocols and more in long-cycle than short-cycle conditions. EMG activity did not increase markedly over time in any condition. Neither objective nor subjective indicators suggested that stress increased over time, regardless of the temporal pattern. Pipetting performance remained stable in all conditions. Cognitive performance, measured by the proportions of correct positive and false positive answers, differed between CTdiff levels but remained stable over time, with no significant difference between temporal patterns. In summary, temporal patterns of alternating tasks influenced fatigue to some extent but had no obvious influence on stress indicators or performance. Thus, designing job rotation with alternating physical and cognitive work should consider the temporal patterns of alternations to minimize fatigue.

Decoding a Music-Modulated Cognitive Arousal State using Electrodermal Activity and Functional Near-infrared Spectroscopy Measurements

Biofeedback systems sense different physiological activities and help with gaining self-awareness. Understanding music's impact on the arousal state is of great importance for biofeedback stress management systems. In this study, we investigate a cognitive-stress-related arousal state modulated by different types of music. During our experiments, each subject was presented with neurological stimuli that elicit a cognitive-stress-related arousal response in a working memory experiment. Moreover, this cognitive-stress-related arousal was modulated by calming and vexing music played in the background. Electrodermal activity and functional near-infrared spectroscopy (fNIRS) measurements both contain information related to cognitive arousal and were collected in our study. By considering various fNIRS features, we selected three features based on variance, root mean square, and local fNIRS peaks as the most informative fNIRS observations in terms of cognitive arousal. The rate of neural impulse occurrence underlying EDA was taken as a binary observation. To retain a low computational complexity for our decoder and select the best fNIRS-based observations, two features were chosen as fNIRS-based observations at a time. A decoder based on one binary and two continuous observations was utilized to estimate the hidden cognitive-stress-related arousal state. This was done by using a Bayesian filtering approach within an expectation-maximization framework. Our results indicate that the decoded cognitive arousal modulated by vexing music was higher than calming music. Among the three fNIRS observations selected, a combination of observations based on root mean square and local fNIRS peaks resulted in the best decoded states for our experimental settings. This study serves as a proof of concept for utilizing fNIRS and EDA measurements to develop a low-dimensional decoder for tracking cognitive-stress-related arousal levels.

Frequent, Short Physical Activity Breaks Reduce Prefrontal Cortex Activation but Preserve Working Memory in Middle-Aged Adults: ABBaH Study

Prolonged sitting is increasingly common and may possibly be unfavorable for cognitive function and mood. In this randomized crossover study, the effects of frequent, short physical activity breaks during prolonged sitting on cognitive task-related activation of the prefrontal cortex were investigated. The effects on working memory, psychological factors, and blood glucose were also examined, and whether arterial stiffness moderated prefrontal cortex activation. Thirteen subjects (mean age 50.5 years; eight men) underwent three 3-h sitting conditions, interrupted every 30-min by a different 3-min break on separate, randomized-ordered days: seated social interactions (SOCIAL), walking (WALK), or simple resistance activities (SRA). Arterial stiffness was assessed at baseline. Before and after each 3-h condition, psychological factors (stress, mood, sleepiness, and alertness) were assessed through questionnaires and functional near-infrared spectroscopy (fNIRS) was used to measure changes in prefrontal oxygenated hemoglobin (Oxy-Hb), indicative of cortical activation, while performing working memory tasks [1- (baseline), 2-, and 3-back]. Blood glucose levels were continuously measured throughout the conditions. Results revealed no significant changes in Oxy-Hb during the 2-back compared with the 1-back test in any condition, and no time-by-condition interactions. During the 3-back test, there was a significant decrease in Oxy-Hb compared with the 1-back after the WALK condition in the right prefrontal cortex, but there were no time-by-condition interactions, although 3-back reaction time improved only in the WALK condition. Mood and alertness improved after the WALK condition, which was significantly different from the SOCIAL condition. Arterial stiffness moderated the effects, such that changes in Oxy-Hb were significantly different between WALK and SOCIAL conditions only among those with low arterial stiffness. Blood glucose during the interventions did not differ between conditions. Thus, breaking up prolonged sitting with frequent, short physical activity breaks may reduce right prefrontal cortex activation, with improvements in some aspects of working memory, mood, and alertness. Clinical Trial Registration:www.ClinicalTrials.gov, identifier NCT04137211.

fNIRS Monitoring of Infant Prefrontal Cortex During Crawling and an Executive Functioning Task

Functional near-infrared spectroscopy (fNIRS)is a brain-imaging technology used to reveal brain activity by measuring blood oxygenation. Using fNIRS we measured activity in the left prefrontal lobe of 8–14 month-old infants as they crawled or were pushed in a stroller and as they were given a passive attention task or an active executive function (EF) task. For each task, we measured peak total hemoglobin concentration and peak Oxy relative to baseline. Results revealed differences in peak Oxy levels for crawling vs. strolling and between the EF and passive cognitive tasks, with total hemoglobin greater for the EF task than the passive attention task. These results support the theoretical view that both active locomotion and EF engage the prefrontal cortex (PFC) during early development.

Working Memory Performance under a Negative Affect Is More Susceptible to Higher Cognitive Workloads with Different Neural Haemodynamic Correlates

The effect of stress on task performance is complex, too much or too little stress negatively affects performance and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload-related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor- and workload-related stress were more dominant in the medial and lateral PFC, respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of a negative effect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.

Decoding different working memory states during an operation span task from prefrontal fNIRS signals

We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.

Increased cognitive workload evokes greater neurovascular coupling responses in healthy young adults

Understanding how the brain allocates resources to match the demands of active neurons under physiological conditions is critically important. Increased metabolic demands of active brain regions are matched with hemodynamic responses known as neurovascular coupling (NVC). Several methods that allow noninvasive assessment of brain activity in humans detect NVC and early detection of NVC impairment may serve as an early marker of cognitive impairment. Therefore, non-invasive NVC assessments may serve as a valuable tool to detect early signs of cognitive impairment and dementia. Working memory tasks are routinely employed in the evaluation of cognitive task-evoked NVC responses. However, recent attempts that utilized functional near-infrared spectroscopy (fNIRS) or transcranial Doppler sonography (TCD) while using a similar working memory paradigm did not provide convincing evidence for the correlation of the hemodynamic variables measured by these two methods. In the current study, we aimed to compare fNIRS and TCD in their performance of differentiating NVC responses evoked by different levels of working memory workload during the same working memory task used as cognitive stimulation. Fourteen healthy young individuals were recruited for this study and performed an n-back cognitive test during TCD and fNIRS monitoring. During TCD monitoring, the middle cerebral artery (MCA) flow was bilaterally increased during the task associated with greater cognitive effort. fNIRS also detected significantly increased activation during a more challenging task in the left dorsolateral prefrontal cortex (DLPFC), and in addition, widespread activation of the medial prefrontal cortex (mPFC) was also revealed. Robust changes in prefrontal cortex hemodynamics may explain the profound change in MCA blood flow during the same cognitive task. Overall, our data support our hypothesis that both TCD and fNIRS methods can discriminate NVC evoked by higher demand tasks compared to baseline or lower demand tasks.

Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

This study aims to decode the hemodynamic responses (HRs) evoked by multiple sound-categories using functional near-infrared spectroscopy (fNIRS). The six different sounds were given as stimuli (English, non-English, annoying, nature, music, and gunshot). The oxy-hemoglobin (HbO) concentration changes are measured in both hemispheres of the auditory cortex while 18 healthy subjects listen to 10-s blocks of six sound-categories. Long short-term memory (LSTM) networks were used as a classifier. The classification accuracy was 20.38 ± 4.63% with six class classification. Though LSTM networks' performance was a little higher than chance levels, it is noteworthy that we could classify the data subject-wise without feature selections.