Human Cognitive Performance Research Articles

A Systematic Investigation Based on BCI and EEG Implemented using Machine Learning Algorithms

BCI is a strong tool that improves human-system communication. It improves the brain's ability to interact with its surroundings. Recent decades have seen substantial advances in neuroscience and computer science. This has made BCI a leader in computational neuroscience and intelligence research. Recent technological advances including wearable sensing devices, real-time data streaming, machine learning, and deep learning have raised the need for electroencephalographic (EEG)-based brain-computer interface (BCI) in clinical and translational applications. EEG-based Brain-Computer Interfaces (BCIs) detect cognitive state variations throughout laborious tasks, making them advantageous for individuals. To fill in the gaps in the wide overview of the past five years (2019-2024), we surveyed the newest research on EEG signal detection and computational intelligence in brain-computer interfaces. To provide a more accurate account, we will begin by reviewing Brain-Computer Interface (BCI) technology and its main challenges. Modern signal detection and enhancement techniques for EEG signal collection and refinement follow. We also provide advanced computational intelligence methods for tracking, maintaining, and monitoring human cognitive and operational performance in everyday applications. Combinations, interpretable fuzzy models, transfer learning, and deep learning are used. We conclude with a sample of cutting-edge BCI-driven healthcare applications and explore future EEG-based BCI research.

Propagating cortical waves coordinate sensory encoding and memory retrieval in the human brain.

Complex behavior entails a balance between taking in sensory information from the environment and utilizing previously learned internal information. Experiments in behaving mice have demonstrated that the brain continually alternates between outward and inward modes of cognition, switching its mode of operation every few seconds. Further, each state transition is marked by a stereotyped cascade of neuronal spiking that pervades most forebrain structures. Here we analyzed large fMRI datasets to demonstrate that a similar switching mechanism governs the operation of the human brain. We found that human brain activity was punctuated every several seconds by coherent, propagating waves emerging in the exteroceptive sensorimotor regions and terminating in the interoceptive default mode network. As in the mouse, the issuance of such events coincided with fluctuations in pupil size, indicating a tight relationship with arousal fluctuations, and this phenomenon occurred across behavioral states. Strikingly, concurrent measurement of human performance in a visual memory task indicated that each cycle of propagating fMRI waves sequentially promoted the encoding of semantic information and self-directed retrieval of memories. Together, these findings indicate that human cognitive performance is governed by autonomous switching between exteroceptive and interoceptive states. This apparently conserved feature of mammalian brain physiology bears directly on the integration of sensory and mnemonic information during everyday behavior.

Exploring Methodological Considerations: A Literature Review on How Lighting Affects the Sleep and Cognition in Healthy Older Adults

The impacts of lighting conditions on human circadian rhythms, sleep quality, and cognitive performance have been extensively investigated in the past two decades; however, these studies have yielded inconclusive and variable outcomes. For older adults who are at a higher risk of developing serious physiological and mental illnesses, such as Alzheimer’s or dementia, light therapy has emerged as a low-risk intervention to improve sleep quality and cognitive function. Nevertheless, the optimal methodology for evaluating the efficacy of light therapy in older adults remains unclear. This review has been conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and critically analyzes methodologies in previous studies on lighting's impact on sleep and cognitive performance in healthy older adults, focusing on how these approaches affect the findings. The review is structured into six domains: study setting and type, participant characteristics, lighting conditions, study design, sleep quality evaluation methods, and cognitive performance evaluation methods. Diverse study designs, methods, and population characteristics have influenced the outcomes. Bright light, applied from early morning to early evening, has been shown to enhance sleep and cognitive functions, notably working memory and concentration. It also benefits from dawn simulation throughout the day, which regulates circadian rhythms and improves sleep quality, although the ideal timing is yet to be determined. Intense short-wavelength lights and strong placebo conditions can counteract these positive effects, and using bright light in the evening may impair sleep and indirectly worsen cognitive performance in older adults. Further real-world experimental studies on this demographic, meticulous study designs, a combination of objective and subjective evaluation methods, and comprehensive reporting of lighting interventions are crucial for identifying the optimal lighting design approach for this population.

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.

Proximity to rewards modulates parameters of effortful control exertion.

The now-classic goal-gradient hypothesis posits that organisms increase effort expenditure as a function of their proximity to a goal. Despite nearly a century having passed since its original formulation, goal-gradient-like behavior in human cognitive performance remains poorly understood: Are we more willing to engage in costly cognitive processing when we are near, versus far, from a goal state? Moreover, the computational mechanisms underpinning these potential goal-gradient effects-for example, whether goal proximity affects fidelity of stimulus encoding, response caution, or other identifiable mechanisms governing speed and accuracy-are unclear. Here, in two experiments, we examine the effect of goal proximity, operationalized as progress toward the completion of a rewarded task block, upon task performance in an attentionally demanding oddball task. Supporting the goal-gradient hypothesis, we found that participants responded more quickly, but not less accurately, when rewards were proximal than when they were distal. Critically, this effect was only observed when participants were given information about goal proximity. Using hierarchical drift diffusion modeling, we found that these apparent goal-gradient performance effects were best explained by a collapsing bound model, in which proximity to a goal reduced response caution and increased information processing. Taken together, these results suggest that goal gradients could help explain the oft-observed fluctuations in engagement of cognitively effortful processing, extending the scope of the goal-gradient hypothesis to the domain of cognitive tasks. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Experimental Evaluation of Noise Exposure Effects on Subjective Perceptions and Cognitive Performance

Individuals exposed to elevated noise levels experience heightened emotional intensity, leading to increased cognitive disruption and a higher likelihood of accidents. This study seeks to investigate the impact of noise exposure on human cognitive performance, and the moderating role of emotion. Twelve healthy male college-age students underwent exposure to three noise conditions, each characterized by different sound pressure levels and sharpness. Each condition included an initial acoustic/thermal adaption period lasting approximately 40 min, followed by intermittent questionnaire tests and a battery of computerized cognitive tests. Statistical analysis revealed that reducing noise levels proved advantageous, enhancing perceived sound quality, positive emotions, and auditory perception abilities, while concurrently reducing false alerts and accelerating execution speed. Many of these effects were found to be counteracted by elevated sharpness. Correlation analyses and partial least squares structural equation modeling (PLS-SEM) results suggested that human emotions mediate the relationship between noise exposure and cognitive performance. The potential underlying mechanism suggests that negative feelings towards noise contribute to poor emotional states, subsequently influencing cognitive processes and impairing executive function. The outcomes of this study provide valuable insights into the mechanism of noise exposure and its effects on human cognition and subjective perceptions.

Investigating the Relationship between Noise Exposure and Human Cognitive Performance: Attention, Stress, and Mental Workload Based on EEG Signals Using Power Spectrum Density

A pervasive environmental stressor is one that damages mental and physical health as well as cognitive abilities by producing noise at a specific frequency and level. Current noise pollution levels pose a significant threat to public health, potentially leading to impaired cognitive function, increased stress, and other negative health consequences. This study aims to investigate the relationship between noise exposure and human cognitive abilities using a comprehensive analysis of power spectrum density (PSD) derived from EEG signals. Twenty-four participants completed the experiment to identify the effect of exposure to different noise levels (55 dB, 65 dB, 70 dB, 75 dB, 80 dB, and 85 dB) and two types of continuous and intermittent noise. The Stroop Color–Word Test and the Emotive Epoch EEG are cognitive task instruments used during experiments. Behavioral performance (accuracy and response time) and power spectrum electroencephalographic density were collected and analyzed. The methodology involved collecting EEG data from participants exposed to controlled noise stimuli and a subsequent PSD analysis to uncover frequency-specific patterns associated with cognitive processes. Attention levels were measured by examining beta wave activity, while stress responses were evaluated through an alpha wave analysis. Additionally, mental workload was assessed by considering the overall distribution of PSD through the theta-to-alpha ratio. The results revealed a significant relationship between the exposure to noise types and levels and human cognitive ability. The analysis of the power spectrum density on the cognitive aspects of attention and stress yielded results indicating that participants were in the best attention condition and in a relaxed or unstressed state when exposed to noise levels of 65 dB in both continuous and intermittent noise types. For the mental workload aspect, participants exposed to both continuous and intermittent noise types at a noise level of 70 dB began to indicate the presence of mental workload. These findings supported the importance of considering the impact of environmental noise on human cognitive well-being and demonstrated the potential of EEG monitoring as an objective tool for assessing the impact of noise on cognitive performance.

Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking Environments.

There is a significant need to monitor human cognitive performance in complex environments, with one example being pilot performance. However, existing assessments largely focus on subjective experiences (e.g., questionnaires) and the evaluation of behavior (e.g., aircraft handling) as surrogates for cognition or utilize brainwave measures which require artificial setups (e.g., simultaneous auditory stimuli) that intrude on the primary tasks. Blink-related oscillations (BROs) are a recently discovered neural phenomenon associated with spontaneous blinking that can be captured without artificial setups and are also modulated by cognitive loading and the external sensory environment-making them ideal for brain function assessment within complex operational settings. Electroencephalography (EEG) data were recorded from eight adult participants (five F, M = 21.1 years) while they completed the Multi-Attribute Task Battery under three different cognitive loading conditions. BRO responses in time and frequency domains were derived from the EEG data, and comparisons of BRO responses across cognitive loading conditions were undertaken. Simultaneously, assessments of blink behavior were also undertaken. Blink behavior assessments revealed decreasing blink rate with increasing cognitive load (p < 0.001). Prototypical BRO responses were successfully captured in all participants (p < 0.001). BRO responses reflected differences in task-induced cognitive loading in both time and frequency domains (p < 0.05). Additionally, reduced pre-blink theta band desynchronization with increasing cognitive load was also observed (p < 0.05). This study confirms the ability of BRO responses to capture cognitive loading effects as well as preparatory pre-blink cognitive processes in anticipation of the upcoming blink during a complex multitasking situation. These successful results suggest that blink-related neural processing could be a potential avenue for cognitive state evaluation in operational settings-both specialized environments such as cockpits, space exploration, military units, etc. and everyday situations such as driving, athletics, human-machine interactions, etc.-where human cognition needs to be seamlessly monitored and optimized.

Surrogate modelling of heartbeat events for improved J-peak detection in BCG using deep learning.

Sleep, or the lack thereof, has far-reaching consequences on many aspects of human physiology, cognitive performance, and emotional wellbeing. To ensure undisturbed sleep monitoring, unobtrusive measurements such as ballistocardiogram (BCG) are essential for sustained, real-world data acquisition. Current analysis of BCG data during sleep remains challenging, mainly due to low signal-to-noise ratio, physical movements, as well as high inter- and intra-individual variability. To overcome these challenges, this work proposes a novel approach to improve J-peak extraction from BCG measurements using a supervised deep learning setup. The proposed method consists of the modeling of the discrete reference heartbeat events with a symmetric and continuous kernel-function, referred to as surrogate signal. Deep learning models approximate this surrogate signal from which the target heartbeats are detected. The proposed method with various surrogate signals is compared and evaluated with state-of-the-art methods from both signal processing and machine learning approaches. The BCG dataset was collected over 17 nights using inertial measurement units (IMUs) embedded in a mattress, together with an ECG for reference heartbeats, for a total of 134h. Moreover, we apply for the first time an evaluation metric specialized for the comparison of event-based time series to assess the quality of heartbeat detection. The results show that the proposed approach demonstrates superior accuracy in heartbeat estimation compared to existing approaches, with an MAE (mean absolute error) of 1.1s in 64-s windows and 1.38s in 8-s windows. Furthermore, it is shown that our novel approach outperforms current methods in detecting the location of heartbeats across various evaluation metrics. To the best of our knowledge, this is the first approach to encode temporal events using kernels and the first systematic comparison of various event encodings for event detection using a regression-based sequence-to-sequence model.

Parasitic manipulation or side effects? The effects of past Toxoplasma gondii and Borrelia spp. infections on human personality and cognitive performance are not mediated by impaired health

Parasitic manipulation or side effects? The effects of past Toxoplasma gondii and Borrelia spp. infections on human personality and cognitive performance are not mediated by impaired health

The Influence of Noise Exposure at Different Loudness Levels on EEG Index and Types of Attention.

Noise is one of the most important harmful factors in the environment. There are limited studies on the effect of noise loudness on brain signals and attention. The main objective of this study was to investigate the relationship between exposure to different loudness levels with brain index, types of attention, and subjective evaluation. Four noises with different loudness levels were generated. Sixty-four male students participated in this study. Each subject performed the integrated visual and auditory continuous performance test (IVA-2) test before and during exposure to noise loudness signals while their electroencephalography was recorded. Finally, the alpha-to-gamma ratio (AGR), five types of attention, and the subjective evaluation results were examined. During exposure to loudness levels, the AGR and types of attention decreased while the NASA-Tax Load Index (NASA-TLX) scores increased. The noise exposure at lower loudness levels (65 and 75 phon) leads to greater attention dysfunction than at higher loudness. The AGR was significantly changed during exposure to 65 and 75 phon and audio stimuli. This significant change was observed in exposure at all loudness levels except 85 phon and visual stimuli. The divided and sustained attention changed significantly during exposure to all loudness levels and visual stimuli. The AGR had a significant inverse correlation with the total score of NASA-TLX during noise exposure. These results can lead to the design of methods to control the psychological effects of noise at specific frequencies (250 and 4000 Hz) and can prevent non-auditory damage to human cognitive performance in industrial and urban environments.

Mental stress recognition on the fly using neuroplasticity spiking neural networks

Mental stress is found to be strongly connected with human cognition and wellbeing. As the complexities of human life increase, the effects of mental stress have impacted human health and cognitive performance across the globe. This highlights the need for effective non-invasive stress detection methods. In this work, we introduce a novel, artificial spiking neural network model called Online Neuroplasticity Spiking Neural Network (O-NSNN) that utilizes a repertoire of learning concepts inspired by the brain to classify mental stress using Electroencephalogram (EEG) data. These models are personalized and tested on EEG data recorded during sessions in which participants listen to different types of audio comments designed to induce acute stress. Our O-NSNN models learn on the fly producing an average accuracy of 90.76% (σ = 2.09) when classifying EEG signals of brain states associated with these audio comments. The brain-inspired nature of the individual models makes them robust and efficient and has the potential to be integrated into wearable technology. Furthermore, this article presents an exploratory analysis of trained O-NSNNs to discover links between perceived and acute mental stress. The O-NSNN algorithm proved to be better for personalized stress recognition in terms of accuracy, efficiency, and model interpretability.

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy.

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5–10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review.

(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort.

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

Tea, coffee and green tea consumption and mental health outcomes: A systematic review and meta-analysis of observational and intervention studies on stress and related conditions

This review aims to examine the effects of tea, coffee, and green tea consumption on human cognitive performance and mental health. Tea, coffee, and green tea are widely consumed beverages worldwide and contain various bioactive compounds that may influence cognitive function and mental health. Research has suggested that caffeine, the primary psychoactive compound in coffee and tea, can improve cognitive function, including attention, alertness, and working memory. The effect of caffeine on cognitive performance may be moderated by individual differences in caffeine metabolism and genetic factors. Green tea contains high levels of catechins, which have been linked to improved cognitive function, such as attention and working memory, as well as reduced risk of cognitive decline and dementia. Theanine, an amino acid found in green tea, has also been associated with relaxation and reduced anxiety. In addition to cognitive performance, tea, coffee, and green tea consumption have been linked to improved mental health outcomes. Research has suggested that coffee consumption may be associated with a lower risk of depression, while green tea consumption has been linked to reduced symptoms of anxiety and stress. However, the evidence regarding the effects of tea, coffee, and green tea on cognitive function and mental health is mixed, and further research is needed to fully understand the potential benefits and risks of these beverages. Additionally, individual differences in caffeine metabolism, genetic factors, and other lifestyle factors may influence the effects of tea, coffee, and green tea on cognitive function and mental health.

Effect of Guarana (Paullinia cupana) on Cognitive Performance: A Systematic Review and Meta-Analysis.

The plant extract guarana is known for its caffeine content and other bioactive ingredients, which purportedly may improve cognitive performance. Recent reviews have examined the effects of chronic supplementation of guarana in clinical populations; however, the acute effects of guarana on cognitive tasks, while of interest, have produced mixed results. Whether acute guarana ingestion improves human cognitive performance was assessed by performing a systematic review coupled with a meta-analysis. Eight placebo-controlled studies were identified and met the inclusion criteria providing data on 328 participants. The dose of guarana (37.5 to 500 mg) with reported caffeine content (4.3 to 100 mg) varied. Effect sizes (ESs) were calculated as the standardized mean difference and meta-analyses were completed using a random-effects model. The ESs for guarana averaged across a variety of cognitive measures and outcome variables were less than trivial (Hedge’s g = 0.076, p = 0.14). Using a subgroup meta-analysis (Q = 12.9, p < 0.001), ESs indicating a faster response time for guarana vs. a placebo (g = 0.202, p = 0.005) differed from the accuracy measures (g = −0.077, p = 0.4) which were non-significant. For response time, guarana ingested in a capsule (g = 0.111) tended to differ (Q = 2.96, p = 0.085) compared to guarana when dissolved in liquid (g = 0.281). Meta-regression of the study ESs of overall cognitive task performance was not related to the guarana dose (R2 < 0.001) or to the time allowed prior to cognitive testing (R2 < 0.001). Acute guarana ingestion had a small effect on the response time (faster performance) during a variety of cognitive tasks without affecting the accuracy. Whether the changes were linked to the caffeine content or other bioavailable substances in guarana is unknown. Additional studies that directly compare matched doses of caffeine versus guarana are needed to understand its effects on cognitive performance.

K-means Clustering Machine Learning Approach Reveals Groups of Homogeneous Individuals with Unique Brain Activation, Task, and Performance Dynamics using fNIRS.

Wearable functional near-infrared spectroscopy (fNIRS) for measuring brain function, in terms of hemodynamic responses, is pervading our everyday life and holds the potential to reliably classify cognitive load in a naturalistic environment. However, human's brain hemodynamic response, behavior, and cognitive and task performance vary, even within and across homogeneous individuals (with same training and skill sets), which limits the reliability of any predictive model for human. In the context of high-stakes tasks, such as in military and first-responder operations, the real-time monitoring of cognitive functions and relating it to the ongoing task, performance outcomes, and behavioral dynamics of the personnel and teams is invaluable. In this work, a portable wearable fNIRS system (WearLight) developed by the author was upgraded, and an experimental protocol was designed to image the prefrontal cortex (PFC) area of the brain of 25 healthy homogeneous participants in a naturalistic environment while participants performed n-back working memory (WM) tasks with four difficulty levels. The raw fNIRS signals were processed using a signal processing pipeline to derive the brain's hemodynamic responses. An unsupervised k-means machine learning (ML) clustering approach, utilizing the task-induced hemodynamic responses as input variables, suggested three unique participant groups. Task performance in terms of % correct, % missing, reaction time, inverse efficiency score (IES), and a proposed IES was extensively evaluated for each participant and the three groups. Results showed that, on average, brain hemodynamic response increased, whereas task performance degraded, with increasing WM load. However, the regression and correlation analysis of WM task, performance, and the brain's hemodynamic responses (TPH) revealedinteresting hidden characteristics and the variation in the TPH relationship between groups. The proposed IES also served as a better scoring method that had distinct score ranges for different load levels as opposed to the overlapping scores of the traditional IES method. Results showed that the k-means clustering has the potential to find groups of individuals in an unsupervised manner using the brain's hemodynamic responses and to study the underlying relationship between the TPH in groups. Using the method presented in this paper,real-time monitoring of cognitive and task performance of soldiers, and preferentially forming small units to accomplish tasks based on the insights and goals may be helpful. The results showed that WearLight can image PFC, and this study also suggests future directions for the multi-modal body sensor network (BSN) combining advanced ML algorithms for real-time state classification, cognitive and physical performance prediction, and the mitigation of performance degradation in the high-stakes environment.

Dynamic hippocampal functional connectivity responses to varying working memory loads following total sleep deprivation.

Sleep loss with work overload can impact human cognitive performance. However, the brain's response to an increased working memory load following total sleep deprivation (TSD) remains unclear. In the present study, we focussed on the dynamic response of the hippocampus to increased working memory load before and after total sleep deprivation of 36 h. A total of 16 male participants completed a verbal working memory task under functional magnetic resonance imaging. After whole-brain activation analysis and region of interest analysis of the hippocampus, the generalised form of context-dependent psychophysiological interactions (gPPI) was used to analyse the hippocampal functional connectivity with the whole brain. The results revealed that as the working memory load increased within a small range, from 0-back to 1-back task, the left hippocampal functional connectivity decreased with the left supplementary motor area, left pars opercularis, left rolandic operculum, right superior frontal gyrus, bilateral precentral gyrus, and left middle cingulate cortex following total sleep deprivation compared with that observed in resting wakefulness. When the working memory load further increased from 1-back to 2-back task, the connectivity increased between the left hippocampus and the left superior parietal lobule as well as between the left hippocampus and right lingual gyrus after total sleep deprivation compared with that observed in resting wakefulness. Moreover, the left hippocampus gPPI effect on the left middle cingulate cortex and left superior parietal lobule could predict the behavioural test accuracy in 1-back and 2-back task, respectively, following total sleep deprivation. These findings indicated that increased working memory load after total sleep deprivation disrupts working memory processes. The brain reacts to these disruptions in a dynamic and flexible manner, involving not only brain activation but also hippocampus-related functional network connections.