Automatic detection of noisy channels in fNIRS signal based on correlation analysis

Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review

With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.

Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research

Neuroergonomics focuses on the brain signatures and associated mental states underlying behavior to design human-machine interfaces enhancing performance in the cognitive and physical domains. Brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have been considered key methods for achieving this goal. Recent research stresses the value of combining EEG and fNIRS in improving these interface systems' mental state decoding abilities, but little is known about whether these improvements generalize over different paradigms and methodologies, nor about the potentialities for using these systems in the real world. We review 33 studies comparing mental state decoding accuracy between bimodal EEG-fNIRS and unimodal EEG and fNIRS in several subdomains of neuroergonomics. In light of these studies, we also consider the challenges of exploiting wearable versions of these systems in real-world contexts. Overall the studies reviewed suggest that bimodal EEG-fNIRS outperforms unimodal EEG or fNIRS despite major differences in their conceptual and methodological aspects. Much work however remains to be done to reach practical applications of bimodal EEG-fNIRS in naturalistic conditions. We consider these points to identify aspects of bimodal EEG-fNIRS research in which progress is expected or desired.

Gender difference in functional activity of 4-months-old infants during sleep: A functional near-infrared spectroscopy study

Sex differences emerge early in infancy. A number of earlier studies have investigated the resting-state network of infant sleep states, and there have been many studies using functional near-infrared spectroscopy (fNIRS) to examine the effects of infant language learning on changes in oxyhemoglobin and deoxyhemoglobin levels. However, studies examining sex differences from the resting-state network of infant sleep states are scarce. This study uses an open access dataset of task-free hemodynamic activity in 4-month-old infants during sleep by fNIRS, to identify some difference between male and female infants. We used Power Spectral Density showing at which frequencies the data variation/variance is high. We have also analyzed some gender differences by analyzing the relationship between individual channels, the degree of activation, etc. The results of this study showed that female and male infants had different Power Spectral Density for oxyhemoglobin and deoxyhemoglobin at rest, showing stronger differences at frontoparietal network, somatomotor network, visual network and dorsal network. This may be due to the differences in the timing or extent of development of those networks. These differences will provide some assistance in future studies of the early education of male and female infants.

A Review of Monitoring Methods for Cerebral Blood Oxygen Saturation

In recent years, cerebral blood oxygen saturation has become a key indicator during the perioperative period. Cerebral blood oxygen saturation monitoring is conducive to the early diagnosis and treatment of cerebral ischemia and hypoxia. The present study discusses the three most extensively used clinical methods for cerebral blood oxygen saturation monitoring from different aspects: working principles, relevant parameters, current situations of research, commonly used equipment, and relative advantages of different methods. Furthermore, through comprehensive comparisons of the methods, we find that near-infrared spectroscopy (NIRS) technology has significant potentials and broad applications prospects in terms of cerebral oxygen saturation monitoring. Despite the current NIRS technology, the only bedside non-invasive cerebral oxygen saturation monitoring technology, still has many defects, it is more in line with the future development trend in the field of medical and health, and will become the main method gradually.

An fNIRS-based investigation of visual merchandising displays for fashion stores

This paper investigates a brain-based approach for visual merchandising display (VMD) in fashion stores. In marketing, VMD has become a research topic of interest. However, VMD research using brain activation information is rare. We examine the hemodynamic responses (HRs) in the prefrontal cortex (PFC) using functional near-infrared spectroscopy (fNIRS) while positive/negative displays of four stores (menswear, womenswear, underwear, and sportswear) are shown to 20 subjects. As features for classifying the HRs, the mean, variance, peak, skewness, kurtosis, t-value, and slope of the signals for a 20-sec time window for the activated channels are analyzed. Linear discriminant analysis is used for classifying two-class (positive and negative displays) and four-class (four fashion stores) models. PFC brain activation maps based on t-values depicting the data from the 16 channels are provided. In the two-class classification, the underwear store had the highest average classification result of 67.04%, whereas the menswear store had the lowest value of 64.15%. Men's classification accuracy for the underwear stores with positive and negative displays was 71.38%, whereas the highest classification accuracy obtained by women for womenswear stores was 73%. The average accuracy over the 20 subjects for positive displays was 50.68%, while that of negative displays was 51.07%. Therefore, these findings suggest that human brain activation is involved in the evaluation of the fashion store displays. It is concluded that fNIRS can be used as a brain-based tool in the evaluation of fashion stores in a daily life environment.

Feedforward- and motor effort-dependent increase in prefrontal oxygenation during voluntary one-armed cranking

KEY POINTS

Some cortical areas are believed to transmit a descending signal in association with motor intention and/or effort that regulates the cardiovascular system during exercise (termed central command). However, there was no evidence for the specific cortical area responding prior to arbitrary motor execution and in proportion to the motor effort. Using a multichannel near-infrared spectroscopy system, we found that the oxygenation of the dorsolateral and ventrolateral prefrontal cortices on the right side increases in a feedforward- and motor effort-dependent manner during voluntary one-armed cranking with the right arm. This finding may suggest a role of the dorsolateral and ventrolateral prefrontal cortices in triggering off central command and may help us to understand impaired regulation of the cardiovascular system in association with lesion of the prefrontal cortex.

ABSTRACT

Output from higher brain centres (termed central command) regulates the cardiovascular system during exercise in a feedforward- and motor effort-dependent manner. This study aimed to determine a cortical area responding prior to arbitrarily started exercise and in proportion to the effort during exercise. The oxygenation responses in the frontal and frontoparietal areas during one-armed cranking with the right arm were measured using multichannel near-infrared spectroscopy, as indexes of regional blood flow responses, in 20 subjects. The intensity of voluntary exercise was 30% and 60% of the maximal voluntary effort (MVE). At the start period of both voluntary cranking tasks, the oxygenation increased (P < 0.05) only in the lateral and dorsal part of the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC) and sensorimotor cortices. Then, the oxygenation increased gradually in all cortical areas during cranking at 60% MVE, while oxygenation increased only in the frontoparietal area and some of the frontal area during cranking at 30% MVE. The rating of perceived exertion to the cranking tasks correlated (P < 0.05) with the oxygenation responses on the right side of the lateral-DLPFC (r = 0.46) and VLPFC (r = 0.48) and the frontopolar areas (r = 0.47-0.49). Motor-driven passive one-armed cranking decreased the oxygenation in most cortical areas, except the contralateral frontoparietal areas. Accordingly, the lateral-DLPFC and VLPFC on the right side would respond in a feedforward- and motor effort-dependent manner during voluntary exercise with the right arm. Afferent inputs from mechanosensitive afferents may decrease the cortical oxygenation.

Cognitive Fatigue, Sleep and Cortical Activity in Multiple Sclerosis Disease. A Behavioral, Polysomnographic and Functional Near-Infrared Spectroscopy Investigation

Patients with multiple sclerosis (MS) disease frequently experience fatigue as their most debilitating symptom. Fatigue in MS partially refers to a cognitive component, cognitive fatigue (CF), characterized by a faster and stronger than usual development of the subjective feeling of exhaustion that follows sustained cognitive demands. The feeling of CF might result from supplementary task-related brain activity following MS-related demyelination and neurodegeneration. Besides, CF in MS disease might also stem from disrupted sleep. The present study investigated the association between the triggering of CF, task-related brain activity and sleep features. In a counterbalance mixed design, 10 patients with MS and 11 healthy controls were exposed twice for 16 min to a CF-inducing dual working memory updating task (TloadDback) under low or high cognitive demands conditions, counterbalanced. Considering known inter-individual differences and potential cognitive deficits in MS, the maximal cognitive load of the task was individually adapted to each participant’s own upper limits. During the experimental sessions, cortical brain activity was measured using near-infrared spectroscopy (NIRS) during the CF-induction task, and in a resting state immediately before and after. Ambulatory polysomnography recordings were obtained on the nights preceding experimental sessions. When cognitive load was individually adapted to their processing capabilities, patients with MS exhibited similar than healthy controls levels of subjectively perceived CF, evolution of performance during the task, and brain activity patterns. Linear mixed models indicate a negative association between oxygenation level changes in the dorsolateral prefrontal cortex (DLPFC) and the triggering of subjective CF in patients with MS only. Longer total sleep time was also associated with higher CF in MS patients. These results suggest that controlling for cognitive load between individuals with and without MS results in a similar task-related development of subjective CF. Besides comparable performance and cortical brain activity between groups, mixed model analyses suggest a possible association between CF, DLPFC activity and sleep duration in MS disease.

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging.

A Brief Review of Research Using Near-Infrared Spectroscopy to Measure Activation of the Prefrontal Cortex during Emotional Processing: The Importance of Experimental Design

During the past two decades there has been a pronounced increase in the number of published research studies that have employed near-infrared spectroscopy (NIRS) to measure neural activation. The technique is now an accepted neuroimaging tool adopted by cognitive neuroscientists to investigate a number of fields, one of which is the study of emotional processing. Crucially, one brain region that is important to the processing of emotional information is the prefrontal cortex (PFC) and NIRS is ideally suited to measuring activity in this region. Compared to other methods used to record neural activation, NIRS reduces the discomfort to participants, makes data collection from larger sample sizes more achievable, and allows measurement of activation during tasks involving physical movement. However, the use of NIRS to investigate the links between emotion and cognition has revealed mixed findings. For instance, whilst some studies report increased PFC activity associated with the processing of negative information, others show increased activity in relation to positive information. Research shows differences in PFC activity between different cognitive tasks, yet findings also vary within similar tasks. This work reviews a selection of recent studies that have adopted NIRS to study PFC activity during emotional processing in both healthy individuals and patient populations. It highlights the key differences between research findings and argues that variations in experimental design could be a contributing factor to the mixed results. Guidance is provided for future work in this area in order to improve consistency within this growing field.