Wearable Functional Near-infrared Spectroscopy Research Articles

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

Cerebral oxygenation and perfusion kinetics monitoring of military aircrew at high G using novel fNIRS wearable system.

Real-time physiological episode (PE) detection and management in aircrew operating high-performance aircraft (HPA) is crucial for the US Military. This paper addresses the unique challenges posed by high acceleration (G-force) in HPA aircrew and explores the potential of a novel wearable functional near-infrared spectroscopy (fNIRS) system, named NIRSense Aerie, to continuously monitor cerebral oxygenation during high G-force exposure. The NIRSense Aerie system is a flight-optimized, wearable fNIRS device designed to monitor tissue oxygenation 13-20 mm below the skin's surface. The system includes an optical frontend adhered to the forehead, an electronics module behind the earcup of aircrew helmets, and a custom adhesive for secure attachment. The fNIRS optical layout incorporates near-distance, middle-distance, and far-distance infrared emitters, a photodetector, and an accelerometer for motion measurements. Data processing involves the modified Beer-Lambert law for computing relative chromophore concentration changes. A human evaluation of the NIRSense Aerie was conducted on six subjects exposed to G-forces up to +9 Gz in an Aerospace Environmental Protection Laboratory centrifuge. fNIRS data, pulse oximetry, and electrocardiography (HR) were collected to analyze cerebral and superficial tissue oxygenation kinetics during G-loading and recovery. The NIRSense Aerie successfully captured cerebral deoxygenation responses during high G-force exposure, demonstrating its potential for continuous monitoring in challenging operational environments. Pulse oximetry was compromised during G-loading, emphasizing the system's advantage in uninterrupted cerebrovascular monitoring. Significant changes in oxygenation metrics were observed across G-loading levels, with distinct responses in Deoxy-Hb and Oxy-Hb concentrations. HR increased during G-loading, reflecting physiological stress and the anti-G straining maneuver. The NIRSense Aerie shows promise for real-time monitoring of aircrew physiological responses during high G-force exposure. Despite challenges, the system provides valuable insights into cerebral oxygenation kinetics. Future developments aim for miniaturization and optimization for enhanced aircrew comfort and wearability. This technology has potential for improving anti-G straining maneuver learning and retention through real-time cerebral oxygenation feedback during centrifuge training.

Effect of automaticity induced by treadmill walking on prefrontal cortex activation and dual-task performance in older adults.

As individuals age, they may experience a decline in gait automaticity, which requires increased attentional resources for the control of gait. This age-related decline in gait automaticity has been shown to contribute to higher prefrontal cortex (PFC) activation and lower dual-task performance during dual-task walking in older adults. This study is to investigate the effect of treadmill walking on PFC activation and dual-task performance in older adults. A total of 20 older adults (mean age, 64.35 ± 2.74 years) and 20 younger adults (mean age, 30.00 ± 3.15 years) performed single- and dual-task walking in overground and treadmill conditions. A wearable functional near-infrared spectroscopy and gait analyzer were used to analyze PFC activation and dual-task performance, respectively. To determine the dual-task (gait and cognitive) performance, the dual-task cost (DTC) was calculated using the following formula: (single-task - dual-task)/single-task × 100. In both groups, dual-task treadmill walking led to reduced PFC activation and reduced DTC compared to dual-task overground walking. Furthermore, despite a higher DTC in gait variability, correct response, total response, response index and a higher error score in older adults than in younger adults during overground walking, there was no difference in treadmill walking. The difference in PFC activation between single- and dual-tasks was also observed only in overground walking. Performing dual-task walking on a treadmill compared to overground walking results in different levels of dual-task performance and PFC activity. Specifically, older adults are able to maintain similar levels of dual-task performance as younger adults while walking on a treadmill, with reduced PFC activation due to the automaticity induced by the treadmill. Therefore, older adults who exhibit low dual-task performance during overground walking may be able to improve their performance while walking on a treadmill with fewer attentional resources.

Combining wearable fNIRS and immersive virtual reality to study preschoolers' social development: a proof-of-principle study on preschoolers' social preference.

A child's social world is complex and rich, but has traditionally been assessed with conventional experiments where children are presented with repeated stimuli on a screen. These assessments are impoverished relative to the dynamics of social interactions in real life, and can be challenging to implement with preschoolers, who struggle to comply with strict lab rules. The current work meets the need to develop new platforms to assess preschoolers' social development, by presenting a unique virtual-reality set-up combined with wearable functional near-infrared spectroscopy (fNIRS). As a proof-of-principle, we validated this platform by measuring brain activity during self-guided social interaction in 3-to-5-year-olds, which is under-investigated, yet crucial to understand the basis of social interactions in preschoolers. 37 preschoolers chose an interaction partner from one of 4 human-like avatars of different gender and age. We recorded spontaneous brain fluctuations from the frontal and temporoparietal regions (notably engaged in social-categorization and preference) while children played a bubble-popping game with a preferred and an assigned avatar. 60% of the participants chose to play with the same-gender and same-age avatar. However, this result was driven by females (>80% vs. 50% in males). Different fronto-temporoparietal connectivity patterns when playing with the two avatars were observed, especially in females. We showed the feasibility of using a novel set-up to naturalistically assess social preference in preschoolers, which was assessed at the behavioural and functional connectivity level. This work provides a first proof-of-principle for using cutting-edge technologies and naturalistic experiments to study social development, opening new avenues of research.

A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment.

Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitative measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics.

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.

Exploring Theater Neuroscience: Using Wearable Functional Near-infrared Spectroscopy to Measure the Sense of Self and Interpersonal Coordination in Professional Actors.

Ecologically valid research and wearable brain imaging are increasingly important in cognitive neuroscience as they enable researchers to measure neural mechanisms of complex social behaviors in real-world environments. This article presents a proof of principle study that aims to push the limits of what wearable brain imaging can capture and find new ways to explore the neuroscience of acting. Specifically, we focus on how to build an interdisciplinary paradigm to investigate the effects of taking on a role on an actor's sense of self and present methods to quantify interpersonal coordination at different levels (brain, physiology, behavior) as pairs of actors rehearse an extract of a play prepared for live performance. Participants were six actors from Flute Theatre, rehearsing an extract from Shakespeare's A Midsummer Night's Dream. Sense of self was measured in terms of the response of the pFC to hearing one's own name (compared with another person's name). Interpersonal coordination was measured using wavelet coherence analysis of brain signals, heartbeats, breathing, and behavior. Findings show that it is possible to capture an actor's pFC response to their own name and that this response is suppressed when an actor rehearses a segment of the play. In addition, we found that it is possible to measure interpersonal synchrony across three modalities simultaneously. These methods open the way to new studies that can use wearable neuroimaging and hyperscanning to understand the neuroscience of social interaction and the complex social-emotional processes involved in theatrical training and performing theater.

Research on the Identification of Pilots’ Fatigue Status Based on Functional Near-Infrared Spectroscopy

Fatigue can lead to sluggish responses, misjudgments, flight illusions and other problems for pilots, which could easily bring about serious flight accidents. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) device was used to record the changes of hemoglobin concentration of pilots during flight missions. The data was pre-processed, and 1080 valid samples were determined. Then, mean value, variance, standard deviation, kurtosis, skewness, coefficient of variation, peak value, and range of oxyhemoglobin (HbO2) in each channel were extracted. These indexes were regarded as the input of a stacked denoising autoencoder (SDAE) and were used to train the identification model of pilots’ fatigue state. The identification model of pilots’ fatigue status was established. The identification accuracy of the SDAE model was 91.32%, which was 23.26% and 15.97% higher than that of linear discriminant analysis (LDA) models and support vector machines (SVM) models, respectively. Results show that the SDAE model established in our study has high identification accuracy, which can accurately identify different fatigue states of pilots. Identification of pilots’ fatigue status based on fNIRS has important practical significance for reducing flight accidents caused by pilot fatigue.

An Energy-Efficient Wearable Functional Near-infrared Spectroscopy System Employing Dual-level Adaptive Sampling Technique.

Functional near-infrared spectroscopy (fNIRS) is a powerful medical imaging tool in brain science and psychology, it can also be employed in brain-computer interface (BCI) due to its noninvasive and artifact-less-sensitive characteristics. Conventional ways to detect large-area brain activity using near-infrared (NIR) technology are based on Time-division or Frequency-division modulation technique, which traverses all physical sensory channels in a specific period. To achieve higher imaging resolution or brain-tasks classification accuracy, the NIRS system require higher density and more channels, which conflict with the limited battery capacity. Inspired by the functional atlas of the human brain, this paper proposes a spatial adaptive sampling (SAS) method. It can change the active channel pattern of the fNIRS system to match with the real-time brain activity, to increase the energy efficiency without significant reduction on the brain imaging quality or the accuracy of brain activity classification. Therefore, the number of the averaging enabled channels will be dramatically reduced in practice. To verify the proposed SAS technique, a wearable and flexible NIRS system has been implemented, in which each channel of light-emitting diode (LED) drive circuits and photodiode (PD) detection circuits can be power gated independently. Brain task experiments have been conducted to validate the proposed method, the power consumption of the LED drive module is reduced by 46.58% compared to that without SAS technology while maintaining an average brain imaging PSNR (Peak Signal to Noise Ratio) of 35 dB. The brain-task classification accuracy is 80.47%, which has a 2.67% reduction compared to that without the SAS technique.

What Does Sleeping Brain Tell About Stress? A Pilot Functional Near-Infrared Spectroscopy Study Into Stress-Related Cortical Hemodynamic Features During Sleep

People with mental stress often experience disturbed sleep, suggesting stress-related abnormalities in brain activity during sleep. However, no study has looked at the physiological oscillations in brain hemodynamics during sleep in relation to stress. In this pilot study, we aimed to explore the relationships between bedtime stress and the hemodynamics in the prefrontal cortex during the first sleep cycle. We tracked the stress biomarkers, salivary cortisol, and secretory immunoglobulin A (sIgA) on a daily basis and utilized the days of lower levels of measured stress as natural controls to the days of higher levels of measured stress. Cortical hemodynamics was measured using a cutting-edge wearable functional near-infrared spectroscopy (fNIRS) system. Time-domain, frequency-domain features as well as nonlinear features were derived from the cleaned hemodynamic signals. We proposed an original ensemble algorithm to generate an average importance score for each feature based on the assessment of six statistical and machine learning techniques. With all channels counted in, the top five most referred feature types are Hurst exponent, mean, the ratio of the major/minor axis standard deviation of the Poincaré plot of the signal, statistical complexity, and crest factor. The left rostral prefrontal cortex (RLPFC) was the most relevant sub-region. Significantly strong correlations were found between the hemodynamic features derived at this sub-region and all three stress indicators. The dorsolateral prefrontal cortex (DLPFC) is also a relevant cortical area. The areas of mid-DLPFC and caudal-DLPFC both demonstrated significant and moderate association to all three stress indicators. No relevance was found in the ventrolateral prefrontal cortex. The preliminary results shed light on the possible role of the RLPCF, especially the left RLPCF, in processing stress during sleep. In addition, our findings echoed the previous stress studies conducted during wake time and provides supplementary evidence on the relevance of the dorsolateral prefrontal cortex in stress responses during sleep. This pilot study serves as a proof-of-concept for a new research paradigm to stress research and identified exciting opportunities for future studies.

Easy Abacus Calculation in Early Childhood to Support Executive Function: An Educational Pilot Case Study of Comparing Brain Activity in the Prefrontal Cortex

The development of executive functions is remarkable in early childhood. Therefore, research on how to support the development of executive functions is actively being conducted. It has already been indicated that executive functions are related to the prefrontal cortex. Recent evidence suggests that the prefrontal cortex is involved in mental abacus (MA). Further, the study of the abacus—the base of MA—is good for not only mathematics but also nurturing the brain. However, although the abacus is easy to learn, learning opportunities have shrunk because of the widespread use of calculators. Through this educational pilot case study, I examined whether it is possible that even easy calculations during the introduction of abacus calculation in early childhood may have an effect on executive function support. I measured the activation of cerebral blood flow in the prefrontal cortex of a young child while he worked on the Wechsler Intelligence Scale for Children-IV; Working Memory Index tasks (forward digit-span task, backward digit-span task, and letter–number sequencing task); and the abacus calculation task using HOT-2000 (NeU, Japan), a two-channel wearable functional near-infrared spectroscopy device. The results revealed a significant difference between the abacus calculation task and the forward digit-span task; however, there was no significant difference between the abacus calculation task and other tasks. In other words, the brain in the prefrontal cortex was more activated in the abacus task than in the forward digit-span task. Difficulty levels were found to be in the order of the forward digit-span task, backward digit-span task, and letter–number sequencing task. Thus, there is a possibility that even simple abacus calculation has a positive effect on executive functions, especially working memory support, in early childhood. This study’s results provide a breakthrough in cognitive psychology, educational psychology, neuropsychology, and other fields related to child support, which are struggling to find valuable, practical practices for children in the field (i.e., schools and homes) beyond the laboratory.

DEMONSTRATING THE FEASIBILITY OF MULTIMODAL NEUROIMAGING DATA CAPTURE WITH A WEARABLE ELECTOENCEPHALOGRAPHY + FUNCTIONAL NEAR-INFRARED SPECTROSCOPY (EEG+FNIRS) IN SITU

AbstractWe developed a wearable experimental sensor setup featuring multimodal EEG+fNIRS neuroimaging applicable for in situ experiments of human behavior in interaction with technology. A low-cost electroencephalography (EEG) was integrated with a wearable functional Near-Infrared Spectroscopy (fNIRS) system, which we present in two parts. Paper A provide an exhaustive description of setup infrastructure, data synchronization process, a procedure for usage, including sensor application, and ensuring high signal quality. This paper (Paper B) demonstrate the setup';s usability in three distinct use cases: a conventional human-computer interaction experiment, an in situ driving experiment where participants drive a car in the city and on the highway, and an ashtanga vinyasa yoga practice in situ. Data on cognitive load from highly ecologically valid experimental setups are presented, and we discuss lessons learned. These include acceptable and unacceptable artefacts, data quality, and constructs possible to investigate with the setup.

Quantified assessment of deep brain stimulation on Parkinson\u2019s patients with task fNIRS measurements and functional connectivity analysis: a pilot study

BackgroundDeep brain stimulation (DBS) has proved effective for Parkinson’s disease (PD), but the identification of stimulation parameters relies on doctors’ subjective judgment on patient behavior.MethodsFive PD patients performed 10-meter walking tasks under different brain stimulation frequencies. During walking tests, a wearable functional near-infrared spectroscopy (fNIRS) system was used to measure the concentration change of oxygenated hemoglobin (△HbO2) in prefrontal cortex, parietal lobe and occipital lobe. Brain functional connectivity and global efficiency were calculated to quantify the brain activities.ResultsWe discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters, and the DBS pattern enabling the highest brain efficiency was optimal for each patient, in accordance with the clinical assessments and DBS treatment decision made by the doctors.ConclusionsTask fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment.Trial registrationName: Accurate treatment under the multidisciplinary cooperative diagnosis and treatment model of Parkinson’s disease. Registration number is ChiCTR1900022715. Date of registration is April 23, 2019.

Frontal lobe oxyhemoglobin levels in patients with lower extremity burns assessed using a functional near-Infrared spectroscopy device during usual walking: a pilot study

Background Understanding the mechanisms associated with locomotor networks may be of benefit for rehabilitation of burn victims with neurological locomotor deficits. A wearable functional near-infrared spectroscopy (fNIRS) device has been developed for studying cortical hemodynamics. Objectives To investigate cortical brain activity during usual walking, we examined patterns of cortical activation using fNIRS device (NIRSIT®; OBELAB Inc., Seoul, Korea), in patients with neurological injury caused by lower extremity burns. Methods This cross-sectional study assessed 15 patients with lower extremity burns, 10 patients with upper extremity burns, and 11 healthy controls. We measured walking-related cortical activity using an fNIRS device at baseline and during usual walking. Results There was no significant difference between the burns groups in terms of age (43.50 ± 14.08 and 44.67 ± 6.92 years, P = 1.00), pain score of NRS (Numeric rating scale) (5.83 ± 1.19 and 6.67 ± 1.21, P = 0.18) or the mean time since injury (228.50 ± 83.43 and 199.33 ± 68.84 days, P = 0.78). Measures showed increased cortical activation in the prefrontal cortex in patients with lower extremity burns than in patients with healthy controls(P = 0.015). The measured HbO2 datas of the regions during usual walking in patients with lower extremity burn were insignificantly higher compared with the datas in patient with upper extremity burn (P = 0.302). Conclusions The patients with neurological injury due to lower extremity burns significantly rely more on cognitive resources even when performing a usual walking task.

Neuroeconomics of Asset-Price Bubbles: Neuroimaging and Digital Technology for the Prediction and Prevention of Major Bubbles

Asset-price bubbles challenge the explanatory and predictive power of standard economic theory, so neuroeconomic measures should be explored as potential tools for improving the predictive power of standard theory. This exploration is begun by reviewing results from functional magnetic resonance imaging (fMRI) studies of lab asset-price bubbles and herding behavior (i.e., following others' decisions). These results are consistent with a neuroeconomics-based hypothesis of asset-price bubbles. In this view, decision making during bubble or non-bubble periods of financial-market activity is driven by, respectively, evolutionarily ancient or new neurocircuitry. Neuroimaging studies that test this or other neuroeconomics-based hypotheses of asset-price bubbles may yield a bubble-related biomarker (e.g., low trade-related lateral neocortical activity associated with traders’ herding-based decisions). Wearable functional near-infrared spectroscopy (fNIRS) technology could determine the prevalence of such a biomarker among financial-market participants, thereby enabling the real-time detection of an emerging bubble. Mechanisms are described by which this early-warning signal could be exploited in self-regulatory or government-administered policies for financial-system stabilization. Digital technology may offer an even more readily achievable alternative to neuroimaging (e.g., behavioral precursors to price bubbles may be identified in analyses of investors’ interactions with asset-trading platforms). In summary, neuroimaging- or digital technology-based financial-system regulation may be useful for distinguishing bubble from non-bubble periods and preventing major asset-price bubbles. To clarify the role of cognitive distortions in these cyclical periods, price is classified as a heuristic that yields bubble or crash biases.

Onset Classification in Hemodynamic Signals Measured During Three Working Memory Tasks Using Wireless Functional Near-Infrared Spectroscopy

Wireless wearable functional near-infrared spectroscopy (fNIRS) has attracted growing attention as a candidate for real-life brain monitoring systems. It is important to determine the onsets at which neuronal activation is evoked by cognitive status in real-time analysis. We propose a machine learning approach for the classification of cognitive event onsets (CogEOs) in hemodynamic signals during three cognitive tasks. The approach does not require a threshold to be set or additional measurement for the rest state. A support vector machine is trained by labeled features obtained from the mean amplitude of hemodynamic changes and then predicts the type of onset points. The problems caused by the imbalance between CogEOs and non-event onsets (NonEO) are solved by oversampling the feature samples labeled by cognitive events. By oversampling, the classification accuracy from an average of five classification scores reaches 74%, 77%, and 75% for the simple arithmetic, 1-back, and 2-back tasks. We achieve the best onset classification performance when the NonEOs are randomly distributed and when the subject is performing the 1-back task. Our study extends fNIRS to real-life applications by detecting the time point when brain activation starts among random observations using machine learning without additional triggers or threshold.

A Review on the Use of Wearable Functional Near-Infrared Spectroscopy in Naturalistic Environments.

The development of novel miniaturized wireless and wearable functional Near-Infrared Spectroscopy (fNIRS) devices have paved the way to new functional brain imaging that can revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow measurements in everyday life scenarios that are not possible with other gold-standard neuroimaging modalities, such as functional Magnetic Resonance Imaging. This can have a huge impact on the way we explore the neural bases and mechanisms underpinning human brain functioning. The aim of this review is to provide an overview of studies conducted with wearable fNIRS in naturalistic settings in the field of cognitive neuroscience. In addition, we present the challenges associated with the use of wearable fNIRS in unrestrained contexts, discussing solutions that will allow accurate inference of functional brain activity. Finally, we provide an overview of the future perspectives in cognitive neuroscience that we believe would benefit the most by using wearable fNIRS.

Rearrangeable and exchangeable optical module with system-on-chip for wearable functional near-infrared spectroscopy system.

.We developed a system-on-chip (SoC)-incorporated light-emitting diode (LED) and avalanche photodiode (APD) modules to improve the usability and flexibility of a fiberless wearable functional near-infrared spectroscopy (fNIRS) system. The SoC has a microprocessing unit and programmable circuits. The time division method and the lock-in method were used for separately detecting signals from different positions and signals of different wavelengths, respectively. Each module autonomously works for this time-divided-lock-in measurement with a high sensitivity for haired regions. By supplying of power and base and data clocks, the LED module emits both 730- and 855-nm wavelengths of light, amplitudes of which are modulated in each lock-in frequency generated from the base clock, and the APD module provides the lock-in detected signals synchronizing with the data clock. The SoC provided many functions, including automatic-power-control of the LED, automatic judgment of detected power level, and automatic-gain-control of the programmable gain amplifier. The number and the arrangement of modules can be adaptively changed by connecting this exchangeable modules in a daisy chain and setting the parameters dependent on the probing position. Therefore, users can configure a variety of arrangements (single- or multidistance combinations) of them with this module-based system.