Bundled-Optode Method in Functional Near-Infrared Spectroscopy

The relationship between the prefrontal cortex and limb motor function in stroke: A study based on resting-state functional near-infrared spectroscopy

BACKGROUND

With the ageing of the world population, the incidence of stroke has been increasing annually, becoming a public health problem affecting adult health. Limb motor dysfunction is one of the common complications of stroke and an important factor in disability. Therefore, restoring limb function is an important task in current rehabilitation. Accurate assessment of motor function in stroke patients is the basis for formulating effective rehabilitation strategies. With the development of neuroimaging technology, scholars have begun to study objective evaluation methods for limb motor dysfunction in stroke to determine reliable neural biomarkers to accurately identify brain functional activity and its relationship with limb motor function. The prefrontal cortex (PFC) plays an important role in motor control and in response to motor state changes. Our previous study found that the PFC network characteristics of stroke patients are related to their motor function status and the topological properties of the PFC network under resting state can predict the motor function of stroke patients to some extent. Therefore, this study used functional near-infrared spectroscopy (fNIRS) to evaluate prefrontal neuroplasticity markers and the relationships between such neural markers and limb motor function in stroke patients with limb motor dysfunction, which could be helpful to further clarify the relationship between brain neuroplasticity and cerebral haemodynamics. At the same time, through accurate and objective means of evaluation, it could be helpful for clinicians to formulate and optimize individualized rehabilitation treatment plans and accurately determine the rehabilitation efficacy and prognosis.

METHODS

This study recruited 17 S patients with limb motor dysfunction and 9 healthy subjects. fNIRS was used to collect 22 channels of cerebral blood oxygen signals in the PFC in the resting state. The differences in prefrontal oxygenated haemoglobin (HbO) and deoxygenated haemoglobin (HbR) concentrations were analysed between stroke patients and healthy subjects, and the lateralization index (LI) of HbO in stroke patients was also calculated. Pearson's correlation analysis was performed between the LI and the scores of the Fugl-Meyer Assessment Scale (FMA) of motor function in stroke patients.

RESULTS

The results found that the prefrontal HbO concentration was significantly decreased in stroke patients with limb motor dysfunction compared with healthy subjects, and there was a significant, positive correlation between the LI of the PFC and FMA scores in stroke patients.

CONCLUSION

These study results showed that stroke can cause cerebral haemodynamic changes in the PFC, and the functional imbalance of the left and right PFC in the resting state is correlated with the severity of limb motor dysfunction. Furthermore, we emphasize that the cerebral haemodynamic activity reflected by fNIRS could be used as a reliable neural biomarker for assessing limb motor dysfunction in stroke.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient

Functional near-infrared spectroscopy (fNIRS) is a type of functional brain imaging. Brain-computer interfaces (BCIs) based on fNIRS have recently been implemented. Most existing fNIRS-BCI studies have involved off-line analyses, but few studies used online performance testing. Furthermore, existing online fNIRS-BCI experimental paradigms have not yet carried out studies using different imagined movements of the same side of a single limb. In the present study, a real-time fNIRS-BCI system was constructed to identify two imagined movements of the same side of a single limb (right forearm and right hand). Ten healthy subjects were recruited and fNIRS signal was collected and real-time analyzed with two imagined movements (leftward movement involving the right forearm and right-hand clenching). In addition to the mean and slope features of fNIRS signals, the correlation coefficient between fNIRS signals induced by different imagined actions was extracted. A support vector machine (SVM) was used to classify the imagined actions. The average accuracy of real-time classification of the two imagined movements was 72.25 ± 0.004%. The findings suggest that different imagined movements on the same side of a single limb can be recognized real-time based on fNIRS, which may help to further guide the practical application of online fNIRS-BCIs.

Review of studies on dynamic cerebral autoregulation in the acute phase of stroke and the relationship with clinical outcome

Acute stroke is associated with high morbidity and mortality. In the last decades, new therapies have been investigated with the aim of improving clinical outcomes in the acute phase post stroke onset. However, despite such advances, a large number of patients do not demonstrate improvement, furthermore, some unfortunately deteriorate. Thus, there is a need for additional treatments targeted to the individual patient. A potential therapeutic target is interventions to optimize cerebral perfusion guided by cerebral hemodynamic parameters such as dynamic cerebral autoregulation (dCA). This narrative led to the development of the INFOMATAS (Identifying New targets FOr Management And Therapy in Acute Stroke) project, designed to foster interventions directed towards understanding and improving hemodynamic aspects of the cerebral circulation in acute cerebrovascular disease states. This comprehensive review aims to summarize relevant studies on assessing dCA in patients suffering acute ischemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage. The review will provide to the reader the most consistent findings, the inconsistent findings which still need to be explored further and discuss the main limitations of these studies. This will allow for the creation of a research agenda for the use of bedside dCA information for prognostication and targeted perfusion interventions.

Optical Modalities for Research, Diagnosis, and Treatment of Stroke and the Consequent Brain Injuries

Stroke is the second most common cause of death and third most common cause of disability worldwide. Therefore, it is an important disease from a medical standpoint. For this reason, various studies have developed diagnostic and therapeutic techniques for stroke. Among them, developments and applications of optical modalities are being extensively studied. In this article, we explored three important optical modalities for research, diagnostic, and therapeutics for stroke and the brain injuries related to it: (1) photochemical thrombosis to investigate stroke animal models; (2) optical imaging techniques for in vivo preclinical studies on stroke; and (3) optical neurostimulation based therapy for stroke. We believe that an exploration and an analysis of previous studies will help us proceed from research to clinical applications of optical modalities for research, diagnosis, and treatment of stroke.

Recent Advances and Current Trends in Brain-Computer Interface (BCI) Research and Their Applications

Brain-Computer Interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, one hundred most cited articles from the WOS database were selected over the last four years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.

Cerebral Autoregulation in Ischemic Stroke: From Pathophysiology to Clinical Concepts

Ischemic stroke (IS) is one of the most impacting diseases in the world. In the last decades, new therapies have been introduced to improve outcomes after IS, most of them aiming for recanalization of the occluded vessel. However, despite this advance, there are still a large number of patients that remain disabled. One interesting possible therapeutic approach would be interventions guided by cerebral hemodynamic parameters such as dynamic cerebral autoregulation (dCA). Supportive hemodynamic therapies aiming to optimize perfusion in the ischemic area could protect the brain and may even extend the therapeutic window for reperfusion therapies. However, the knowledge of how to implement these therapies in the complex pathophysiology of brain ischemia is challenging and still not fully understood. This comprehensive review will focus on the state of the art in this promising area with emphasis on the following aspects: (1) pathophysiology of CA in the ischemic process; (2) methodology used to evaluate CA in IS; (3) CA studies in IS patients; (4) potential non-reperfusion therapies for IS patients based on the CA concept; and (5) the impact of common IS-associated comorbidities and phenotype on CA status. The review also points to the gaps existing in the current research to be further explored in future trials.

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

Background

Brain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.

Objective

This study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.

Method

A systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.

Results

Sixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.

Conclusion

An appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

BACKGROUND

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors.

OBJECTIVE

This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals.

METHODS

In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration.

RESULTS

There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input.

CONCLUSION

The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

Comparison of Human Social Brain Activity During Eye-Contact With Another Human and a Humanoid Robot

Robot design to simulate interpersonal social interaction is an active area of research with applications in therapy and companionship. Neural responses to eye-to-eye contact in humans have recently been employed to determine the neural systems that are active during social interactions. Whether eye-contact with a social robot engages the same neural system remains to be seen. Here, we employ a similar approach to compare human-human and human-robot social interactions. We assume that if human-human and human-robot eye-contact elicit similar neural activity in the human, then the perceptual and cognitive processing is also the same for human and robot. That is, the robot is processed similar to the human. However, if neural effects are different, then perceptual and cognitive processing is assumed to be different. In this study neural activity was compared for human-to-human and human-to-robot conditions using near infrared spectroscopy for neural imaging, and a robot (Maki) with eyes that blink and move right and left. Eye-contact was confirmed by eye-tracking for both conditions. Increased neural activity was observed in human social systems including the right temporal parietal junction and the dorsolateral prefrontal cortex during human-human eye contact but not human-robot eye-contact. This suggests that the type of human-robot eye-contact used here is not sufficient to engage the right temporoparietal junction in the human. This study establishes a foundation for future research into human-robot eye-contact to determine how elements of robot design and behavior impact human social processing within this type of interaction and may offer a method for capturing difficult to quantify components of human-robot interaction, such as social engagement.

Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between "like" vs. "dislike" out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.

Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals

An intrinsic problem when using hemodynamic responses for the brain-machine interface is the slow nature of the physiological process. In this paper, a novel method that estimates the oxyhemoglobin changes caused by neuronal activations is proposed and validated. In monitoring the time responses of blood-oxygen-level-dependent signals with functional near-infrared spectroscopy (fNIRS), the early trajectories of both oxy- and deoxy-hemoglobins in their phase space are scrutinized. Furthermore, to reduce the detection time, a prediction method based upon a kernel-based recursive least squares (KRLS) algorithm is implemented. In validating the proposed approach, the fNIRS signals of finger tapping tasks measured from the left motor cortex are examined. The results show that the KRLS algorithm using the Gaussian kernel yields the best fitting for both ΔHbO (i.e., 87.5%) and ΔHbR (i.e., 85.2%) at q = 15 steps ahead (i.e., 1.63 s ahead at a sampling frequency of 9.19 Hz). This concludes that a neuronal activation can be concluded in about 0.1 s with fNIRS using prediction, which enables an almost real-time practice if combined with EEG.

Co-localization of theta-band activity and hemodynamic responses during face perception: simultaneous electroencephalography and functional near-infrared spectroscopy recordings

Face-specific neural processes in the human brain have been localized to multiple anatomical structures and associated with diverse and dynamic social functions. The question of how various face-related systems and functions may be bound together remains an active area of investigation. We hypothesize that face processing may be associated with specific frequency band oscillations that serve to integrate distributed face processing systems. Using a multimodal imaging approach, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), simultaneous signals were acquired during face and object picture viewing. As expected for face processing, hemodynamic activity in the right occipital face area (OFA) increased during face viewing compared to object viewing, and in a subset of participants, the expected N170 EEG response was observed for faces. Based on recently reported associations between the theta band and visual processing, we hypothesized that increased hemodynamic activity in a face processing area would also be associated with greater theta-band activity originating in the same area. Consistent with our hypothesis, theta-band oscillations were also localized to the right OFA for faces, whereas alpha- and beta-band oscillations were not. Together, these findings suggest that theta-band oscillations originating in the OFA may be part of the distributed face-specific processing mechanism.

Effects of Acupuncture Therapy on MCI Patients Using Functional Near-Infrared Spectroscopy

Acupuncture therapy (AT) is a non-pharmacological method of treatment that has been applied to various neurological diseases. However, studies on its longitudinal effect on the neural mechanisms of patients with mild cognitive impairment (MCI) for treatment purposes are still lacking in the literature. In this clinical study, we assess the longitudinal effects of ATs on MCI patients using two methods: (i) Montreal Cognitive Assessment test (MoCA-K, Korean version), and (ii) the hemodynamic response (HR) analyses using functional near-infrared spectroscopy (fNIRS). fNIRS signals of a working memory (WM) task were acquired from the prefrontal cortex. Twelve elderly MCI patients and 12 healthy people were recruited as target and healthy control (HC) groups, respectively. Each group went through an fNIRS scanning procedure three times: The initial data were obtained without any ATs, and subsequently a total of 24 AT sessions were conducted for MCI patients (i.e., MCI-0: the data prior to ATs, MCI-1: after 12 sessions of ATs for 6 weeks, MCI-2: another 12 sessions of ATs for 6 weeks). The mean HR responses of all MCI-0–2 cases were lower than those of HCs. To compare the effects of AT on MCI patients, MoCA-K results, temporal HR data, and spatial activation patterns (i.e., t-maps) were examined. In addition, analyses of functional connectivity (FC) and graph theory upon WM tasks were conducted. With ATs, (i) the averaged MoCA-K test scores were improved (MCI-1, p = 0.002; MCI-2, p = 2.9e^–4); (ii) the mean HR response of WM tasks was increased (p < 0.001); and (iii) the t-maps of MCI-1 and MCI-2 were enhanced. Furthermore, an increased FC in the prefrontal cortex in both MCI-1/MCI-2 cases in comparison to MCI-0 was obtained (p < 0.01), and an increasing trend in the graph theory parameters was observed. All these findings reveal that ATs have a positive impact on improving the cognitive function of MCI patients. In conclusion, ATs can be used as a therapeutic tool for MCI patients as a non-pharmacological method (Clinical trial registration number: KCT 0002451 https://cris.nih.go.kr/cris/en/).

Use of fNIRS to Characterize the Neural Mechanism of Inter-Individual Rhythmic Movement Coordination

Background: Inter-individual rhythmic movement coordination plays an important role in daily life, particularly in competitive sports. Behaviorally, it is more challenging to coordinate alternating movements than symmetrical movements. The neural activity underlying these different movement coordination modes remains to be clarified, particularly considering complex inter-individual coordination differences.

Methods: To further test the neural basis of inter-individual rhythmic movement coordination, a revised experimental paradigm of inter-individual coordination was adopted. Participants were asked to perform symmetric, alternate, or single movements (swinging the lower part of the leg) in the same rhythm. A multi-channel, continuous wave, functional near-infrared spectral (fNIRS) imaging instrument was used to monitor hemodynamic activity while 40 volunteers (9 male pairs and 11 female pairs) performed the task. Multivariate analyses of variance were conducted to compare mean oxy-hemoglobin concentration ([HbO]) across experimental conditions.

Results: A significant three-way interaction (leg-swing condition × ROI × laterality) on mean [HbO] was observed. Post hoc analysis revealed a significant main effect of leg-swing condition only in brain regions of interest [right inferior parietal lobule (IPL)] contralateral to movement execution. Activation in brain regions of interest [right inferior parietal lobule (IPL)] was much stronger in alternate mode compared with symmetric or single modes, and the differences between symmetric and single mode were not statistically significant. This result suggests that the alternate mode of movement coordination was more likely to be supported by the IPL region than the other modes.

Conclusion: The present findings provide neural evidence relevant to the theory of self-organization of movement coordination, in which an alternating movement mode appeared to be a more demanding condition than symmetrical movement.

Functional Spectroscopy Mapping of Pain Processing Cortical Areas During Non-painful Peripheral Electrical Stimulation of the Accessory Spinal Nerve

Peripheral electrical stimulation (PES), which encompasses several techniques with heterogeneous physiological responses, has shown in some cases remarkable outcomes for pain treatment and clinical rehabilitation. However, results are still mixed, mainly because there is a lack of understanding regarding its neural mechanisms of action. In this study, we aimed to assess its effects by measuring cortical activation as indexed by functional near infrared spectroscopy (fNIRS). fNIRS is a functional optical imaging method to evaluate hemodynamic changes in oxygenated (HbO) and de-oxygenated (HbR) blood hemoglobin concentrations in cortical capillary networks that can be related to cortical activity. We hypothesized that non-painful PES of accessory spinal nerve (ASN) can promote cortical activation of sensorimotor cortex (SMC) and dorsolateral prefrontal cortex (DLPFC) pain processing cortical areas. Fifteen healthy volunteers received both active and sham ASN electrical stimulation in a crossover study. The hemodynamic cortical response to unilateral right ASN burst electrical stimulation with 10 Hz was measured by a 40-channel fNIRS system. The effect of ASN electrical stimulation over HbO concentration in cortical areas of interest (CAI) was observed through the activation of right-DLPFC (p = 0.025) and left-SMC (p = 0.042) in the active group but not in sham group. Regarding left-DLPFC (p = 0.610) and right-SMC (p = 0.174) there was no statistical difference between groups. As in non-invasive brain stimulation (NIBS) top-down modulation, bottom-up electrical stimulation to the ASN seems to activate the same critical cortical areas on pain pathways related to sensory-discriminative and affective-motivational pain dimensions. These results provide additional mechanistic evidence to develop and optimize the use of peripheral nerve electrical stimulation as a neuromodulatory tool (NCT 03295370— www.clinicaltrials.gov).

A Systemic Review of Functional Near-Infrared Spectroscopy for Stroke: Current Application and Future Directions

Background: Survivors of stroke often experience significant disability and impaired quality of life. The recovery of motor or cognitive function requires long periods. Neuroimaging could measure changes in the brain and monitor recovery process in order to offer timely treatment and assess the effects of therapy. A non-invasive neuroimaging technique near-infrared spectroscopy (NIRS) with its ambulatory, portable, low-cost nature without fixation of subjects has attracted extensive attention. Methods: We conducted a comprehensive literature review in order to review the use of NIRS in stroke or post-stroke patients in July 2018. NCBI Pubmed database, EMBASE database, Cochrane Library and ScienceDirect database were searched. Results: Overall, we reviewed 66 papers. NIRS has a wide range of application, including in monitoring upper limb, lower limb recovery, motor learning, cortical function recovery, cerebral hemodynamic changes, cerebral oxygenation, as well as in therapeutic method, clinical researches, and evaluation of the risk for stroke. Conclusions: This study provides a preliminary evidence of the application of NIRS in stroke patients as a monitoring, therapeutic, and research tool. Further studies could give more emphasize on the combination of NIRS with other techniques and its utility in the prevention of stroke.

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.

Adaptive filtering of physiological noises in fNIRS data

The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.

Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study

Enhanced classification accuracy and a sufficient number of commands are highly demanding in brain computer interfaces (BCIs). For a successful BCI, early detection of brain commands in time is essential. In this paper, we propose a novel classifier using a modified vector phase diagram and the power of electroencephalography (EEG) signal for early prediction of hemodynamic responses. EEG and functional near-infrared spectroscopy (fNIRS) signals for a motor task (thumb tapping) were obtained concurrently. Upon the resting state threshold circle in the vector phase diagram that uses the maximum values of oxy- and deoxy-hemoglobin (ΔHbO and ΔHbR) during the resting state, we introduce a secondary (inner) threshold circle using the ΔHbO and ΔHbR magnitudes during the time window of 1 s where an EEG activity is noticeable. If the trajectory of ΔHbO and ΔHbR touches the resting state threshold circle after passing through the inner circle, this indicates that ΔHbO was increasing and ΔHbR was decreasing (i.e., the start of a hemodynamic response). It takes about 0.5 s for an fNIRS signal to cross the resting state threshold circle after crossing the EEG-based circle. Thus, an fNIRS-based BCI command can be generated in 1.5 s. We achieved an improved accuracy of 86.0% using the proposed method in comparison with the 63.8% accuracy obtained using linear discriminant analysis in a window of 0~1.5 s. Moreover, the active brain locations (identified using the proposed scheme) were spatially specific when a t-map was made after 10 s of stimulation. These results demonstrate the possibility of enhancing the classification accuracy for a brain-computer interface with a time window of 1.5 s using the proposed method.

Optimal positioning of optodes on the scalp for personalized functional near-infrared spectroscopy investigations

BACKGROUND

Application of functional Near InfraRed Spectroscopy (fNIRS) in neurology is still limited as a good optical coupling and optimized optode coverage of specific brain regions remains challenging, notably for prolonged monitoring.

METHODS

We propose to evaluate a new procedure allowing accurate investigation of specific brain regions. The procedure consists in: (i) A priori maximization of spatial sensitivity of fNIRS measurements targeting specific brain regions, while reducing the number of applied optodes in order to decrease installation time and improve subject comfort. (ii) Utilization of a 3D neuronavigation device and usage of collodion to glue optodes on the scalp, ensuring good optical contact for prolonged investigations. (iii) Local reconstruction of the hemodynamic activity along the cortical surface using inverse modelling.

RESULTS

Using realistic simulations, we demonstrated that maps derived from optimal montage acquisitions showed, after reconstruction, spatial resolution only slightly lower to that of ultra high density montages while significantly reducing the number of optodes. The optimal montages provided overall good quantitative accuracy especially at the peak of the spatially reconstructed map. We also evaluated real motor responses in two healthy subjects and obtained reproducible motor responses over different sessions.

COMPARISON WITH EXISTING METHODS

We are among the first to propose a mathematical optimization strategy, allowing high sensitivity measurements.

CONCLUSIONS

Our results support that using personalized optimal montages should allow to conduct accurate fNIRS studies in clinical settings and realistic lifestyle conditions.

Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided.

Neuronal Activation Detection Using Vector Phase Analysis with Dual Threshold Circles: A Functional Near-Infrared Spectroscopy Study

In this paper, a new vector phase diagram differentiating the initial decreasing phase (i.e. initial dip) and the delayed hemodynamic response (HR) phase of oxy-hemoglobin changes ( Δ HbO) of functional near-infrared spectroscopy (fNIRS) is developed. The vector phase diagram displays the trajectories of Δ HbO and deoxy-hemoglobin changes ( Δ HbR), as orthogonal components, in the Δ HbO- Δ HbR polar coordinates. To determine the occurrence of an initial dip, dual threshold circles (an inner circle from the resting state, an outer circle from the peak values of the initial dip and the main HR) are incorporated into the phase diagram for making decisions. The proposed scheme is then applied to a brain-computer interface scheme, and its performance is evaluated in classifying two finger tapping tasks (right-hand thumb and little finger) from the left motor cortex. Three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. In classifying two tapping tasks, the signal mean and signal minimum values during 0-2.5 s, as features of initial dip, are used. The linear discriminant analysis was utilized as a classifier. The experimental results show that the active brain locations of the two tasks were quite distinctive ( p < 0.05 ), and moreover, spatially specific if using the initial dip map at 4 s in comparison to the map of HRs at 14 s. Also, the average classification accuracy was improved from 59% to 74.9% when using the phase diagram of dual threshold circles.

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh-Nagumo Neurons under Direction-Dependent Coupling

This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh–Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the synchronization of uncertain and perturbed neurons, communicating in a medium of bidirectional coupling. The neurons are assumed to be different and interconnected in a ring structure. The strength of the gap junctions is taken to be different for each link in the network, owing to the inter-neuronal coupling medium properties. Robust adaptive control mechanism based on Lyapunov stability analysis is employed and theoretical criteria are derived to realize the synchronization of the network of four FHN neurons in a ring form with unknown parameters under direction-dependent coupling and disturbances. The proposed scheme for synchronization of dissimilar neurons, under external electrical stimuli, coupled in a ring communication topology, having all parameters unknown, and subject to directional coupling medium and perturbations, is addressed for the first time as per our knowledge. To demonstrate the efficacy of the proposed strategy, simulation results are provided.

An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results.

Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive), direct-to-peripheral-nerve (invasive), and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals. The peripheral nerve interfaces are highly dependent on interface designs and are required to be biocompatible with the nerves to achieve prolonged stability and longevity. Another criterion is the selection of nerves that allows minimal invasiveness and damages as well as high selectivity for a large number of nerve fascicles. In this paper, we review the nerve-machine interface modalities noted above with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback. The invasive interfaces for recording and stimulation of electro-neurographic signals include intra-fascicular, regenerative-type interfaces that provide multiple contact channels to a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable interaction with many axons around the periphery of the nerve. Section Current Prosthetic Technology summarizes the advancements made to date in the field of neuroprosthetics toward the achievement of a bidirectional nerve-machine interface with more focus on sensory feedback. In the Discussion section, the authors propose a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques.

Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain–computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

Classification of somatosensory cortex activities using fNIRS

The ability of the somatosensory cortex in differentiating various tactile sensations is very important for a person to perceive the surrounding environment. In this study, we utilize a lab-made multi-channel functional near-infrared spectroscopy (fNIRS) to discriminate the hemodynamic responses (HRs) of four different tactile stimulations (handshake, ball grasp, poking, and cold temperature) applied to the right hand of eight healthy male subjects. The activated brain areas per stimulation are identified with the t-values between the measured data and the desired hemodynamic response function. Linear discriminant analysis is utilized to classify the acquired data into four classes based on three features (mean, peak value, and skewness) of the associated oxy-hemoglobin (HbO) signals. The HRs evoked by the handshake and poking stimulations showed higher peak values in HbO than the ball grasp and cold temperature stimulations. For comparison purposes, additional two-class classifications of poking vs. temperature and handshake vs. ball grasp were performed. The attained classification accuracies were higher than the corresponding chance levels. Our results indicate that fNIRS can be used as an objective measure discriminating different tactile stimulations from the somatosensory cortex of human brain.

Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control

In this paper, a hybrid electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain–computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG–fNIRS interface.

Detection and classification of three-class initial dips from prefrontal cortex

In this paper, the use of initial dips using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI) is investigated. Features and window sizes for detecting initial dips are also discussed. Three mental tasks including mental arithmetic, mental counting, and puzzle solving are performed in obtaining fNIRS signals from the prefrontal cortex. Vector-based phase analysis method combined with a threshold circle, as a decision criterion, are used to detect the initial dips. Eight healthy subjects participate in experiment. Linear discriminant analysis is used as a classifier. To classify initial dips, five features (signal mean, peak value, signal slope, skewness, and kurtosis) of oxy-hemoglobin (HbO) and four different window sizes (0~1, 0~1.5, 0~2, and 0~2.5 sec) are examined. It is shown that a combination of signal mean and peak value and a time period of 0~2.5 sec provide the best average classification accuracy of 57.5% for three classes. To further validate the result, three-class classification using the conventional hemodynamic response (HR) is also performed, in which two features (signal mean and signal slope) and 2~7 sec window size have yielded the average classification accuracy of 65.9%. This reveals that fNIRS-based BCI using initial dip detection can reduce the command generation time from 7 sec to 2.5 sec while the classification accuracy is a bit sacrificed from 65.9% to 57.5% for three mental tasks. Further improvement can be made by using deoxy hemoglobin signals in coping with the slow HR problem.