fNIRS Data Research Articles

Classification of fNIRS Finger Tapping Data With Multi-Labeling and Deep Learning

Studying the relationship between the brain and finger tapping motions can contribute towards an improved understanding of neuromuscular impairment. Furthermore, acquiring brain data signals non-intrusively during finger tapping exercises, and building a robust classification model can aid in the field of human computer interaction. In this paper, we present a promising approach for spatially descriptive multi-labeling of spatiotemporal functional Near Infrared Spectroscopy (fNIRS) data to autonomously detect different finger tapping levels in different regions of the brain simultaneously. Our multi-class multi-labeling technique assigns labels to the left and right index fingers, and a given label describes one of three different finger tapping frequencies (rest, 80bpm, and 120bpm) to be monitored in the corresponding contralateral spatial location in the brain’s motor cortex. We train a CNN/LSTM-based network to classify the aforementioned finger tapping levels spatially and simultaneously. The evaluation, based on simultaneous multi-label predictions for two brain regions, is performed with a metric commonly used in multi-labeling, Hamming Loss, along with confusion matrix-based measurements. Promising testing results are obtained with an average Hamming Loss of 0.185, average F-Score of 0.81, and average Accuracy of 0.81. Moreover, we explain our model and novel multi-labeling approach by generating Shapley Additive Explanation values and plotting them on an image-like background, which represents the fNIRS channel layout used as data input. Shapley values help to add interpretability to our deep learning model and by confirming expected results, offer a pathway to the future development of complex deep learning models that attempt to predict social-cognitive-affective states.

FNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control

Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control.

FNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases.

.Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy.Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia.Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM).Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR () over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups.Conclusions: can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.

A guide for the use of fNIRS in microcephaly associated to congenital Zika virus infection

Congenital Zika Syndrome (CZS) is characterized by changes in cranial morphology associated with heterogeneous neurological manifestations and cognitive and behavioral impairments. In this syndrome, longitudinal neuroimaging could help clinicians to predict developmental trajectories of children and tailor treatment plans accordingly. However, regularly acquiring magnetic resonance imaging (MRI) has several shortcomings besides cost, particularly those associated with childrens' clinical presentation as sensitivity to environmental stimuli. The indirect monitoring of local neural activity by non-invasive functional near-infrared spectroscopy (fNIRS) technique can be a useful alternative for longitudinally accessing the brain function in children with CZS. In order to provide a common framework for advancing longitudinal neuroimaging assessment, we propose a principled guideline for fNIRS acquisition and analyses in children with neurodevelopmental disorders. Based on our experience on collecting fNIRS data in children with CZS we emphasize the methodological challenges, such as clinical characteristics of the sample, desensitization, movement artifacts and environment control, as well as suggestions for tackling such challenges. Finally, metrics based on fNIRS can be associated with established clinical metrics, thereby opening possibilities for exploring this tool as a long-term predictor when assessing the effectiveness of treatments aimed at children with severe neurodevelopmental disorders.

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.

Getting in synch: Unpacking the role of parent-child synchrony in the development of internalizing and externalizing behaviors.

While substantial research supports the role of parent-child interactions on the emergence of psychiatric symptoms, few studies have explored biological mechanisms for this association. The current study explored behavioral and neural parent-child synchronization during frustration and play as predictors of internalizing and externalizing behaviors across a span of 1.5 years. Parent-child dyads first came to the laboratory when the child was 4-5 years old and completed the Disruptive Behavior Diagnostic Observation Schedule: Biological Synchrony (DB-DOS: BioSync) task while functional near-infrared spectroscopy (fNIRS) data were recorded. Parents reported on their child's internalizing and externalizing behaviors using the Child Behavior Checklist (CBCL) four times over 1.5 years. Latent growth curve (LGC) modeling was conducted to assess neural and behavioral synchrony as predictors of internalizing and externalizing trajectories. Consistent with previous investigations in this age range, on average, internalizing and externalizing behaviors decreased over the four time points. Parent-child neural synchrony during a period of play predicted rate of change in internalizing but not externalizing behaviors such that higher parent-child neural synchrony was associated with a more rapid decrease in internalizing behaviors. Our results suggest that a parent-child dyad's ability to coordinate neural activation during positive interactions might serve as a protective mechanism in the context of internalizing behaviors.

The Effect of Yoga Meditation Practice on Young Adults' Inhibitory Control: An fNIRS Study.

Objectives: The present study aimed to test the effect of yoga meditation (YoMed) practice on inhibitory control of young adults.Methods: A total of 50 participants (23 male, 21–28 years old) from a university in Jinan, Shandong Province were enrolled in this study. Participants were randomly assigned to a YoMed group or a Control group. Participants’ basic information, physical activity, and inhibitory control were measured. A multi-channel continuous-wave near-infrared spectrometer was used to monitor the brain’s hemodynamic responses.Results: After the intervention, we found significant differences in Flanker tasks between the YoMed group and Control group. The accuracy in the YoMed group was higher than those in the Control group (p < 0.05). Analysis of fNIRS data showed that oxyhemoglobin (oxy-Hb) levels in the prefrontal cortex (PFC) increased in the YoMed group during the Flanker tasks after the YoMed intervention.Conclusion: YoMed has a temporarily promoting effect on the brain activation of young adults. It is an effective and appropriate exercise to improve the inhibitory control of young adults.

Wearable, Integrated EEG-fNIRS Technologies: A Review.

There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.

Development and Feasibility Testing of a Brief Yoga Module on Well-being and Cognition of Postgraduate Mental Healthcare Students in Tertiary Settings.

Background:Mental health-care students experience stress and burnout during their training period. Yoga has been found to be helpful in improving one's mental health and well-being.Aim:The aim of this study is to develop and test the feasibility of a brief yoga module for postgraduate mental health-care students.Methods:Amixed method design was used. Phase 1 involved development and validation of the yoga module using the qualitative exploratory method. Phase II tested the feasibility of the module on a sample of 28 first-year postgraduate students. These students participated in a 15-day (30 min/day) brief yoga module for improving their well-being and cognition (mirror neuron activation [MNA] and tower of London task as assessed on day 0, day 15 (2 weeks), and day 30 (4 weeks). Qualitative feedback of the student volunteers was also recorded.Results:Significant improvement in the well-being scores was observed in the students who adhered to the yoga practice after 2 weeks and 4 weeks. Functional near infra-red spectroscopy (fNIRS) data indicated that adherents showed significant activation of left somatosensory region of the brain and deactivation in the right primary somatosensory region during the static and active phase of the MNA task, respectively. Adherent group showed significant improvement in reaction time during “Zero-Moves” tasks of Tower of London. The qualitative thematic analysis showed that the module helped improve the well-being and mental health of the students.Conclusion:The yoga program was found to have high need and medium to high feasibility. A systemic integration of student well-being-oriented interventions including yoga in the curriculum of postgraduate mental health-care courses is advocated.

Evaluation of hemodynamic response function during mental arithmetic task in fNIRS data using GLM method

To understand the complex structure of neural networks in the brain, which is a critical concept in Neuroscience, it is essential to identify different areas of the brain. Studies on the performance of various brain regions and their processing techniques have helped collect a large amount of data about the functions of these areas, which can provide deeper insights into the mechanism of this system. In line with this, the present study seeks to evaluate the hemodynamic response function (HRF) of the cerebral cortex in 8 healthy subjects (3 males and 5 females) during mental arithmetic task (Subtraction of single-digit numbers by two-digit numbers). This is conducted by data recording using functional near-infrared spectroscopy (fNIRS) with 52 channels to identify the active parts of their brain. Following the task (3 or 4 runs), we recorded the brain channel signals of the subjects, removing the noise from the extracted signals using wavelet transform technique. Then, the location of the channels and the active areas of the brain were identified in each individual and the group levels using e general linear model (GLM) method. The findings suggest that at the individual level, dorsolateral prefrontal cortex (DLPFC), supramarginal gyrus (SMG), left superior parietal lobule (left SPL), and right inferior frontal gyrus are activated during the mental arithmetic task (subtraction). Also, at the group level, the frontal lobe (dorsolateral prefrontal cortex (DLPFC)) and the parietal lobe (left superior parietal lobule (left SPL) are activated, which is compatible with brain activation during mental arithmetic task.

Acupuncture With deqi Modulates the Hemodynamic Response and Functional Connectivity of the Prefrontal-Motor Cortical Network

As a world intangible cultural heritage, acupuncture is considered an essential modality of complementary and alternative therapy to Western medicine. Despite acupuncture’s long history and public acceptance, how the cortical network is modulated by acupuncture remains largely unclear. Moreover, as the basic acupuncture unit for regulating the central nervous system, how the cortical network is modulated during acupuncture at the Hegu acupoint is mostly unclear. Here, multi-channel functional near-infrared spectroscopy (fNIRS) data were recorded from twenty healthy subjects for acupuncture manipulation, pre- and post-manipulation tactile controls, and pre- and post-acupuncture rest controls. Results showed that: (1) acupuncture manipulation caused significantly increased acupuncture behavioral deqi performance compared with tactile controls. (2) The bilateral prefrontal cortex (PFC) and motor cortex were significantly inhibited during acupuncture manipulation than controls, which was evidenced by the decreased power of oxygenated hemoglobin (HbO) concentration. (3) The bilateral PFC’s hemodynamic responses showed a positive correlation trend with acupuncture behavioral performance. (4) The network connections with bilateral PFC as nodes showed significantly increased functional connectivity during acupuncture manipulation compared with controls. (5) Meanwhile, the network’s efficiency was improved by acupuncture manipulation, evidenced by the increased global efficiency and decreased shortest path length. Taken together, these results reveal that a cooperative PFC-Motor functional network could be modulated by acupuncture manipulation at the Hegu acupoint. This study provides neuroimaging evidence that explains acupuncture’s neuromodulation effects on the cortical network.

Functional Connectivity Within the Fronto-Parietal Network Predicts Complex Task Performance: A fNIRS Study.

Performance in complex tasks is essential for many high risk operators. The achievement of such tasks is supported by high-level cognitive functions arguably involving functional activity and connectivity in a large ensemble of brain areas that form the fronto-parietal network. Here we aimed at determining whether the functional connectivity at rest within this network could predict performance in a complex task: the Space Fortress video game. Functional Near Infrared Spectroscopy (fNIRS) data from 32 participants were recorded during a Resting-State period, the completion of a simple version of Space Fortress (monotask) and the original version (multitask). The intrinsic functional connectivity within the fronto-parietal network (i.e., during the Resting-State) was a significant predictor of performance at Space Fortress multitask but not at its monotask version. The same pattern was observed for the functional connectivity during the task. Our overall results suggest that Resting-State functional connectivity within the fronto-parietal network could be used as an intrinsic brain marker for performance prediction of a complex task achievement, but not for simple task performance.

Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data.

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology’s economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with Brain-Computer Interfaces (BCIs). fNIRS data are multidimensional and complex, making them ideal for deep learning algorithms for classification. Deep Learning classifiers typically need a large amount of data to be appropriately trained without over-fitting. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the accuracy of the deep learning classifier when the sample size is insufficient. The proposed system uses a CGAN with a CNN classifier to enhance the accuracy through data augmentation. The system can determine whether the subject’s task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 96.67% for the CGAN-CNN combination.

Secondary rewards acquire enhanced incentive motivation via increasing anticipatory activity of the lateral orbitofrontal cortex

The motivation to strive for and consume primary rewards such as palatable food is bound by devaluation mechanisms, yet secondary rewards such as money may not be bound by these regulatory mechanisms. The present study therefore aimed at determining diverging devaluation trajectories for primary (chocolate milk) and secondary (money) reinforcers on the behavioral and neural level. Devaluation procedures withrepeated exposure to reward combined with a choice (Experiment 1) and an incentive delay (Experiment 2) paradigm consistently revealed decreasing hedonic value for the primary reward as reflected by decreasing hedonic evaluation and choice preferencewith repeated receipt, while hedonic value and preferences for the secondary reward increased. Concomitantly acquired functional near-infrared spectroscopy (fNIRS) data during the incentive delay paradigm revealed that increasing value of the secondary reward was accompanied by increasing anticipatory activation in the lateral orbitofrontal cortex, while during the consummatory phase the secondary reinforcer associated with higher medial orbitofrontal activity irrespective of devaluation stage. Overall, the findings suggest that-in contrast to primary reinforcers-secondary reinforcers, i.e. money, can acquire progressively enhanced incentive motivation with repeated receipt, suggesting a mechanism which could promote escalating striving to obtain secondary rewards.

Frontotemporal activation differs between perception of simulated cochlear implant speech and speech in background noise: An image-based fNIRS study

In this study we used functional near-infrared spectroscopy (fNIRS) to investigate neural responses in normal-hearing adults as a function of speech recognition accuracy, intelligibility of the speech stimulus, and the manner in which speech is distorted. Participants listened to sentences and reported aloud what they heard. Speech quality was distorted artificially by vocoding (simulated cochlear implant speech) or naturally by adding background noise. Each type of distortion included high and low-intelligibility conditions. Sentences in quiet were used as baseline comparison. fNIRS data were analyzed using a newly developed image reconstruction approach. First, elevated cortical responses in the middle temporal gyrus (MTG) and middle frontal gyrus (MFG) were associated with speech recognition during the low-intelligibility conditions. Second, activation in the MTG was associated with recognition of vocoded speech with low intelligibility, whereas MFG activity was largely driven by recognition of speech in background noise, suggesting that the cortical response varies as a function of distortion type. Lastly, an accuracy effect in the MFG demonstrated significantly higher activation during correct perception relative to incorrect perception of speech. These results suggest that normal-hearing adults (i.e., untrained listeners of vocoded stimuli) do not exploit the same attentional mechanisms of the frontal cortex used to resolve naturally degraded speech and may instead rely on segmental and phonetic analyses in the temporal lobe to discriminate vocoded speech.

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics.

The nature of firefighters’ duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of and an accuracy of if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of and accuracy of when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy.

.Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space.Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis.Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data.Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants.

.Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism-induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain’s organization can be shaped by early environmental factors.Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations.Methods: We measured spontaneous hemodynamic brain activity in a large cohort () of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons.Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age.Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development.

Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation.

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

A low-cost, wearable, do-it-yourself functional near-infrared spectroscopy (DIY-fNIRS) headband

Neuromonitoring in naturalistic environments is of increasing interest for a variety of research fields including psychology, economics, and productivity. Among functional neuromonitoring modalities, functional near-infrared spectroscopy (fNIRS) is well regarded for its potential for miniaturization, good spatial and temporal resolutions, and resilience to motion artifacts. Historically, the large size and high cost of fNIRS systems have precluded widespread adoption of the technology. In this article, we describe the first open source, fully integrated wireless fNIRS headband system with a single LED-pair source and four detectors. With ease of operation and comfort in mind, the system is encased in a soft, lightweight cloth and silicone enclosure. Accompanying computer and smartphone data collection software have also been provided, and the hardware has been validated using classic fNIRS tasks. This wear-and-go design can easily be scaled to accommodate a larger number of fNIRS channels and opens the door to easily collecting fNIRS data during routine activities in naturalistic conditions.