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Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) can modulate human brain dynamics and cognition. However, these modalities have not been compared using multiple imaging techniques concurrently. In this study, 15 participants participated in an experiment involving two sessions with a gap of 10 days. In the first and second sessions, tACS and tDCS were administered to the participants. The anode for tDCS was positioned at point FpZ, and four cathodes were positioned over the left and right prefrontal cortices (PFCs) to target the frontal regions simultaneously. tDCS was administered with 1 mA current. tACS was supplied with a current of 1 mA (zero-to-peak value) at 10 Hz frequency. Stimulation was applied concomitantly with functional near-infrared spectroscopy and electroencephalography acquisitions in the resting-state. The statistical test showed significant alteration (p0.001) in the mean hemodynamic responses during and after tDCS and tACS periods. Between-group comparison revealed a significantly less (p0.001) change in the mean hemodynamic response caused by tACS compared with tDCS. As hypothesized, we successfully increased the hemodynamics in both left and right PFCs using tDCS and tACS. Moreover, a significant increase in alpha-band power (p0.01) and low beta band power (p0.05) due to tACS was observed after the stimulation period. Although tDCS is not frequency-specific, it increased but not significantly (p0.05) the powers of most bands including delta, theta, alpha, low beta, high beta, and gamma. These findings suggest that both hemispheres can be targeted and that both tACS and tDCS are equally effective in high-definition configurations, which may be of clinical relevance.

Background: Early diagnosis of Alzheimer’s disease (AD) is essential in preventing its progression to dementia. Mild cognitive impairment (MCI) can be indicative of early-stage AD. In this study, we propose a channel-wise feature extraction method of functional near-infrared spectroscopy (fNIRS) data to diagnose MCI when performing cognitive tasks, including two-back, Stroop, and semantic verbal fluency tasks (SVFT). Methods: A new channel-wise feature extraction method is proposed as follows: A region-of-interest (ROI) channel is defined as such channel having a statistical difference (p Results: In the two-back and Stroop tasks, HCs showed activation in the ventrolateral prefrontal cortex (VLPFC). However, in the case of MCI, the VLPFC was not activated. Instead, Ch. 30 was activated. In the SVFT task, the PFC was activated in both groups, but the t-values of HCs were higher than those of MCI. For the SVFT, the classification accuracies using the proposed feature extraction method were 80.77% (LDA) and 83.33% (SVM), showing the highest among the three tasks; for the Stroop task, 79.49% (LDA) and 73.08% (SVM); and for the two-back task, 73.08% (LDA) and 69.23% (SVM). Conclusion: The cognitive disparities between the MCI and HC groups were detected in the ventrolateral prefrontal cortex using fNIRS. The proposed feature extraction method has shown an improvement in the classification accuracies, see Subsection 3.3. Most of all, the suggested method contains a groupdistinction information per cognitive task. The obtained results successfully discriminated MCI patients from HCs, which reflects that the proposed method is an efficient tool to extract features in fNIRS signals.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive method for acquiring hemodynamic signals from the brain with advantages of portability, affordability, low susceptibility to noise, and moderate temporal resolution that serves as a plausible solution to real-time imaging. fNIRS is an emerging brain imaging technique that measures brain activity by means of near-infrared light of 600–1000 nm wavelengths. Recently, there has been a surge of studies with fNIRS for the acquisition, decoding, and regulation of hemodynamic signals to investigate their behavioral consequences for the implementation of brain–machine interfaces (BMI). In this review, first, the existing methods of fNIRS signal processing for decoding brain commands for BMI purposes are reviewed. Second, recent developments, applications, and challenges faced by fNIRS-based BMIs are outlined. Third, current trends in fNIRS in combination with other imaging modalities are summarized. Finally, we propose a feedback control concept for the human brain, in which fNIRS, electroencephalography, and functional magnetic resonance imaging are considered sensors and stimulation techniques are considered actuators in brain therapy.

In this paper, the most effective stimulation durations in the visual, somatosensory, and motor cortices are investigated. To evoke hemodynamic responses (HRs) for the purpose of brain-computer interface (BCI) with functional near-infrared spectroscopy (fNIRS), the best and the minimal durations for three tasks (visual, tapping, and poking) are presented. The examined tasks include the checkerboard, right-hand-index-finger poking, and the right-hand-index-finger tapping tasks in association with the visual, somatosensory, and motor cortices. Upon stimulations, the peak value, time to peak, and the full width at half maximum of the HRs are examined. Six different stimuli durations (i.e., 1, 3, 5, 7, 10 and 15 sec) were tested. Ten male subjects participated in the experiment. Three types of stimulations having the same duration were presented to the subjects randomly. The stimulation durations that showed the maximum peak values in the checkerboard, poking, and tapping tasks were 7, 5, and 10 sec, respectively. The peak value increased with the increase of stimulation duration. But beyond a certain period of time, the peak value did not increase anymore and sometimes it decreased when the stimulation period is too long. It is noted that 1-sec stimulation (for all three tasks) can generate a noticeable peak value. Instead, the initial dip upon poking occurred only with 1-sec poking (i.e., initial dip did not occur in all other poking periods). Conclusively, this work reveals the possibility of a BCI command generation within 1 sec.

Transcranial direct current stimulation (tDCS) has been shown to create neuroplasticity in healthy and diseased populations. The control of stimulation duration by providing real-time brain state feedback using neuroimaging is a topic of great interest. This study presents the feasibility of a closed-loop modulation for the targeted functional network in the prefrontal cortex. We hypothesize that we cannot improve the brain state further after reaching a specific state during a stimulation therapy session. A high-definition tDCS of 1[Formula: see text]mA arranged in a ring configuration was applied at the targeted right prefrontal cortex of 15 healthy male subjects for 10[Formula: see text]min. Functional near-infrared spectroscopy was used to monitor hemoglobin chromophores during the stimulation period continuously. The correlation matrices obtained from filtered oxyhemoglobin were binarized to form subnetworks of short- and long-range connections. The connectivity in all subnetworks was analyzed individually using a new quantification measure of connectivity percentage based on the correlation matrix. The short-range network in the stimulated hemisphere showed increased connectivity in the initial stimulation phase. However, the increase in connection density reduced significantly after 6[Formula: see text]min of stimulation. The short-range network of the left hemisphere and the long-range network gradually increased throughout the stimulation period. The connectivity percentage measure showed a similar response with network theory parameters. The connectivity percentage and network theory metrics represent the brain state during the stimulation therapy. The results from the network theory metrics, including degree centrality, efficiency, and connection density, support our hypothesis and provide a guideline for feedback on the brain state. The proposed neuro-feedback scheme is feasible to control the stimulation duration to avoid overdosage.

In this paper, a deep-learning-based driver-drowsiness detection for brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS) is investigated. The passive brain signals from drowsiness were acquired from 13 healthy subjects while driving a car simulator. The brain activities were measured with a continuous-wave fNIRS system, in which the prefrontal and dorsolateral prefrontal cortices were focused. Deep neural networks (DNN) were pursued to classify the drowsy and alert states. For training and testing the models, the convolutional neural networks (CNN) were used on color map images to determine the best suitable channels for brain activity detection in 0~1, 0~3, 0~5, and 0~10 second time windows. The average accuracies (i.e., 82.7, 89.4, 93.7, and 97.2% in the 0~1, 0~3, 0~5, and 0~10 sec time windows, respectively) using DNNs from the right dorsolateral prefrontal cortex were obtained. The CNN architecture resulted in an average accuracy of 99.3%, showing the model to be capable of differentiating the images of drowsy/non-drowsy states. The proposed approach is promising for detecting drowsiness and in accessing the brain location for a passive BCI.

Diagnosis of Alzheimer’s disease (AD) in early stage is important to prevent progression of dementia in the aging society. Mild cognitive impairment (MCI) denotes an early stage of AD. In this paper, we aim to diagnose MCI patients during semantic verbal fluency tasks (SVFT) using functional near-infrared spectroscopy (fNIRS). To achieve our objective, t-values and correlations were calculated to find the region of interest (ROI) channels and brain connectivities. From the ROI channels HbO data were averaged over subjects, features (mean, slope, and skewness) were extracted for classification. Extracted features were labelled as two classes and classified via two classifiers, linear discriminant analysis (LDA) and support vector machine (S VM). The classification accuracies were 69.23 % for LDA and 73.07 % for SVM. The results show that there are significant differences of hemodynamic responses (HR) between MCI patients and healthy controls (HC). Therefore, these results suggest a possibility of using fNIRS as a diagnostic tool for MCI patients.

Motor imagery (MI) is the most favorable brain action exploited in motor deficit rehabilitation. The hemodynamic response (HR) signal is usually weak, which can be enhanced with specific methods. In this research, the improvement in the HR was examined while the participants performed the MI task of right-hand movement. Brain signals, i.e., concentration changes in oxyhemoglobin ( $\Delta\text{HbO}$ ) from the left sensorimotor cortex, were attained through functional near-infrared spectroscopy. The experiment was executed in 2 sessions. The first session consisted of the performance of the MI task without visual training. Whereas in the last session, the MI task was performed but with the visual training: A videotape was played on the computer screen that presented a person moving his right hand, repeatedly; the purpose was to help in enhancing the imaginations and, therefore, improving the HR. The t-values between the measured HR and the designed hemodynamics response function were calculated to check certain channels' activeness. Later, the trials of active channels were averaged to obtain mean $\Delta\text{HbO}$ . MI task with visual training showed elevated activation in $\Delta\text{HbO}$ . Furthermore, linear discriminant analysis was utilized to classify signals by taking the mean and maximum of HR resulting in mean classification accuracy of 65% and 80% for MI tasks with- and without visual training. These results reflected an enhancement in MI ability through visual training, which will help patients' rehabilitation and BCI applications.

To date, several studies have utilized brain stimulation to evoke particular brain activity. One of the most critical questions that remain unaddressed is how long the stimulation should last. To address this issue, this paper investigates two different stimulation durations for braincomputer interface (BCI) with functional near-infrared spectroscopy (fNIRS). To do so, this paper investigates the influence of the stimulation duration on the hemodynamic response (HR) signal in the sensorimotor cortex of the human brain. fNIRS is used for the measurement of HRs. For brain stimulation, right-hand index finger tapping and poking tasks are utilized, and the HR signals are acquired. Two different stimulation durations, i.e., 10 and 15 secs, were utilized in this study, and a total of 5 subjects participated in the experiment. After acquiring the signals, signals are filtered, leading to classification. From the study results, it is concluded that the classification accuracy does not increase with the increase in the stimulation duration among the tested stimulation durations. So, the use of longer stimulation durations, i.e., 15 secs, can't serve the purpose well and make the subjects tired. In contrast, 10-sec stimulation yielded a higher classification accuracy in a comparatively shorter time, which is the BCI's core objective.

BackgroundBrain 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.ObjectiveThis 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.MethodA 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.ResultsSixty-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.ConclusionAn 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.

Functional near-infrared spectroscopy (fNIRS) can help to diagnose specific diseases or distinguish motions for brain-computer interface (BCI). Also, repeating the same experiment can be uncomfortable for participants. It is difficult for researchers to obtain enough measurements to train the classification model sufficiently, which results in unstable classification accuracy. In this study, we investigated how to expand fNIRS data using the deep convolutional generative adversarial network (DCGAN) to improve classification accuracy and training stability. The data were measured using fNIRS during the finger tapping tasks, and then the proposed data augmentation method was used for generating artificial fNIRS datasets. These data were used to train a convolutional neural network (CNN), CNN model gave final classification accuracies for two classes (i. e. thumb finger tapping and little finger tapping). The AlexNet model, which is one famous model of the well-made model structure, was used as a CNN classifier model, the acquired accuracy for distinguishing the two types of tasks has been improved compared with the obtained accuracy using original data. This result suggests that the proposed deep learning model as a data generator is useful for improving classification performance.

In this paper, the most effective stimulation durations for the purpose of brain-computer interface (BCI) with functional near-infrared spectroscopy (fNIRS) is investigated. To do so, this study investigates the influence of varying stimulation duration on the hemodynamic response (HR) signal in the primary visual cortex of the human brain. fNIRS is used for the measurement of HRs. For brain stimulation, a flickering checkerboard task is utilized, and the HR signals are acquired. Three different stimulation durations, i.e., 1, 3, and 5 secs were used in this study, and a total of 5 subjects participated in the experiment. From the results of the study, it is concluded that among the tested stimulation durations, the HR signal rises with the increase of the stimulation duration. Also, it was found that even the smallest stimulation duration, i.e., 1 sec, can generate a detectable activation in the visual cortex of the brain, which can well serve the purpose of BCI.

In this paper, we investigate the effect of stimulation durations on the hemodynamic responses (HRs) in the somatosensory cortex. In doing so, the relationship between stimulation duration and the initial dip is also investigated. The HRs are measured using functional near-infrared spectroscopy (fNIRS). The HR signals related to finger poking are acquired from the left somatosensory cortex. Two different stimulation durations (i.e., 1 and 5 sec) were tested in this study. From the results of the study, it is concluded that the stimulation duration of 1 sec (short stimulus) evokes initial dip in the somatosensory cortex, but it disappears as the stimulation duration gets longer. Therefore, the 1-sec stimulation duration can serve the purpose of the fNIRS-based brain-computer interface.

Background: Mild cognitive impairment (MCI) is a precursor to the critical disease known as Alzheimer’s. It is imperative to develop a proper treatment for this neurological disease in the aging society. This study investigates the effects of acupuncture therapy (AT) on MCI patients.Methods: Eleven healthy individuals and eleven MCI patients were recruited for this study. Oxy- and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy. Before the AT, working-memory experiments were conducted for healthy control (HC) and MCI groups (MCI-0), followed by 24 sessions of AT for the MCI group. The AT sessions were initially carried out for six weeks (two sessions per week), after which experiments were performed again on the MCI group (MCI-1). This was followed by another set of AT sessions that also lasted for six weeks, after which the experiments were repeated again on the MCI group (MCI-2). Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.Results: The highest classification accuracies obtained using binary connectivity maps were 85.7% HC vs. MCI-0, 69.5% HC vs. MCI-1, and 61.69% HC vs. MCI-2. The classification accuracies using the temporal features mean (i.e., mean(5:28 s)) and maximum (i.e., max(5:28 s)) values were 60.6% HC vs. MCI-0, 56.9% HC vs. MCI-1, and 56.4% HC vs. MCI-2. Conclusions: The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture. This was reflected by a reduction in the classification accuracy after the therapy, indicating that the patients’ brain response improved and became comparable to those of healthy subjects. Similar trend was reflected in the classification using image feature. These results indicate that acupuncture can be used for the treatment of MCI patients.Trial registration: Clinical research and information service (CRIS), KCT 0002451, Registered 05 September 2017, https://cris.nih.go.kr/cris/en/

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 thirteen 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 s ~ 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.

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.

Diagnosis of dementia in early stage is important to prevent progression of dementia in the aging society. Mild cognitive impairment (MCI) denotes an early stage of Alzheimer disease (AD). In this paper, we aim to classify MCI patients from healthy controls (HC) during working memory tasks using functional near-infrared spectroscopy (fNIRS). To achieve this objective, t-values and correlation coefficients are calculated to find the region of interest (ROI) channels and brain connectivity. From the ROI channels averaged over subjects, features (mean and slope) of hemodynamic responses were extracted for classification. Extracted features were labelled as two classes and classified via two classifiers, linear discriminant analysis (LDA) and support vector machine (SVM). The classification accuracies were 73.08 % with LDA and 71.15 % with SVM. The results show that there are significant differences in the hemodynamic responses (HR) between MCI patients and healthy controls. Therefore, these results suggest a possibility of using fNIRS as a diagnostic tool for MCI patients.

With growing age, the cognitive ability degrades gradually as an aging factor. For a portion of people, the cognitive capability diminishes to a great extent, which will eventually result in Alzheimer's disease (AD). Mild cognitive impairment (MCI) is considered as an intermediate stage of AD. Diagnosis of AD patients at an early stage can reduce the chance of developing into a severe condition for cognition. This study aims to investigate the MCI assessment methods (statistical analysis and individual classification) for distinguishing the healthy control (HC) and MCI patients via functional near-infrared spectroscopy (fNIRS). This study evaluated ten digital biomarkers from three brain regions and three mental tasks ( $N$ -back, Stroop, and verbal fluency task). Among these three tasks, the $N$ -back task achieved the best accuracy (76.67 %) with biomarker 2 (HbO mean from 5 to 25 sec) and 7 (HbO slope from 0 to peak value) in the middle prefrontal cortex by linear discriminant analysis (LDA). Additionally, the statistical analysis results also indicated that a significant difference ( $p$ -value $p$ -value < 0.05. It reveals that statistical analysis technique still should be improved for diagnosing MCI individuals. Machine learning (LDA) can contribute as a tool by early prediction of AD via analyzing digital biomarkers using a non-invasive technique.

A decline in cognition characterized by the deficit in memory, judgment, and language is known as mild cognitive impairment (MCI). This deficiency has great likelihood of developing Alzheimer’s disease (AD). Hence, the recognition of MCI or an early stage of AD is critical at earliest for evaluation and treatment. In this study, functional near-infrared spectroscopy (fNIRS) was used to measure the concentration changes of oxygenated hemoglobin (ΔHbO) from the pre-frontal cortex of six healthy controls (HC) and six MCI patients during a working memory task. Various temporal features of ΔHbO, such as mean, slope, kurtosis, skewness, and peak-values, were tested for distinguishing MCI patients from HCs. We further investigated the impact of intervention (i.e., acupuncture treatment) on MCI patients (i.e., MCI-1: before intervention, MCI-2: after intervention). Both groups went through fNIRS recording procedure with a gap of 45 days. Before the second fNIRS measurement, only the patient group underwent interventions twice per week for six weeks. The results showed that the mean ΔHbO response of MCI-1 was significantly less than (p 0.05) with MCI-2. Out of tested temporal features, signal mean and peak values of HC were significantly different (p = 0.0409, p = 0.0430) in comparison to MCI-1 but no significant difference (p = 0.4885, p = 0.4437) if MCI-2. Moreover, these fNIRS results were positively correlated with cognitive test scores. These preliminary results showed that fNIRS can be used for distinguishing MCI patients from HCs using mean hemodynamic response as well as temporal features.

One of the most promising brain activity utilized in brain-computer interface (BCI) is motor imagery (MI). Due to weak hemodynamic response (HR) signal, the achieved classification accuracies using MI are not sufficiently high. In this study, the enhancement in HR was investigated during motor imagery tasks of ball squeezing with the right hand. Brain signals in the form of concentration changes in oxy-hemoglobin (ΔHbO) and deoxy-hemoglobin (ΔHbR) from the left sensorimotor cortex were obtained using functional near-infrared spectroscopy (fNIRS). The experiment was separated in two sessions: In the first session the MI task was performed without a visual aid, and in the second session of the same task, the visual aid was provided: A video was played on a screen that showed a person continuously squeezing the ball, which can help in enhancing the imagination, thus improvement in HR. Later the features of averaged ΔHbO were used for classification. The active channels were selected on the basis of t-values and trials of those channels were mean to obtain averaged ΔHbO. Consistent with literature, imagery task with visual aid, showed increased activation in ΔHbO. Moreover, linear discriminant analysis was used to classify signals by taking the mean and peak of the averaged ΔHbO resulting in average classification accuracies of approximately 66% and 77% for MI task, with and without visual aid, respectively. These results are convincing that showed improvement in MI ability which will be useful for fNIRS-based BCI applications.

The risk of getting psychiatric disease or various kinds of dementia is rising as a significant problem in the aging society. Functional near-infrared spectroscopy (fNIRS) can measure the blood chromophores noninvasively for early diagnosis, and frequent examination, which are vital in case of brain degeneration. We present the development and functioning of our lab-developed fNIRS system. It provides a real-time display of variation in blood chromophores related to neuronal activation. It employs 128 dual-wavelength LEDs of 735 nm and 850 nm. The selection of these wavelengths allows computation of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). A single photosensor is used in the presented device. This system is developed using a modular approach where a single module can cover approximately 7 cm x 7 cm while multiple modules can be used to cover a wider area. The current configuration utilizes different source-detector separation to reach multiple depths between 2 cm and 3.5 cm. Short separation channels also exist in the design to provide the information of superficial layers. MOSFET based LED switching is implemented that allows sharp current switching for high-speed data acquisition. Windows-based software is developed for the display of fNIRS data in real time. Wi-Fi is used as the wireless medium of communication between the hardware and software. Phantom model, as well as human subject, was used for testing the device efficacy. The phantom results showed that by increasing the channel-separation, the signal intensity was reduced. Resting state human subject was also evaluated to compute and display the HbO in real time. A complete fNIRS sample comprising of 128 channels was recorded in 25 ms.

Owing to the advancements in the field of brain technology, numerous researches have been done to explore the prospects of application of brain signals in numerous ways. This work presents a novel approach to control a quadcopter using brain activity for eye blinking signals. EEG signals generated as a result of eye blinking are transformed into useful input to the quadcopter to perform respective commands. The work covers the execution of 8 commands using a 4-sec time window via 2 EEG electrodes. Each task to be executed in a 1-sec window. For more fast and easier control, an additional interface is provided to make the commands handier and more useful for the subject to choose between the commands. Classification accuracy shows promising results which can ensure better control for other devices like a quadcopter. Thus, this work provides a user-friendly approach for control of a quadcopter via eye blinking signals using a low cost and non-invasive technique using a wearable EEG headset.

Mild cognitive impairment (MCI), a condition characterizing poor cognition, is associated with aging and depicts early symptoms of severe cognitive impairment, known as Alzheimer’s disease (AD). Meanwhile, early detection of MCI can prevent progression to AD. A great deal of research has been performed in the past decade on MCI detection. However, availability of biomarkers for MCI detection requires greater attention. In our study, we evaluated putative and reliable biomarkers for diagnosing MCI by performing different mental tasks (i.e., N-back task, Stroop task, and verbal fluency task) using functional near-infrared spectroscopy (fNIRS) signals on a group of 15 MCI patients and 9 healthy control (HC). The 15 digital biomarkers (i.e., five means, seven slopes, peak, skewness, and kurtosis) and two image biomarkers (t-map, correlation map) in the prefrontal cortex (PFC) (i.e., left PFC, middle PFC, and right PFC) between the MCI and HC groups were investigated by the statistical analysis, linear discriminant analysis (LDA), and convolutional neural network (CNN) individually. The results reveal that the statistical analysis using digital biomarkers (with a p-value < 0.05) could not distinguish the MCI patients from the HC over 60% accuracy. Therefore, the current statistical analysis needs to be improved to be used for diagnosing the MCI patients. The best accuracy with LDA was 76.67% with the N-back and Stroop tasks. However, the CNN classification results trained by image biomarkers showed a high accuracy. In particular, the CNN results trained via t-maps revealed the best accuracy (90.62%) with the N-back task, whereas the CNN result trained by the correlation maps was 85.58% with the N-back task. Also, the results illustrated that investigating the sub-regions (i.e., right, middle, left) of the PFC for detecting MCI would be better than examining the whole PFC. The t-map (or/and the correlation map) is conclusively recommended as an image biomarker for early detection of AD. The combination of CNN and image biomarkers can provide a reliable clinical tool for diagnosing MCI patients.

Acupuncture therapy (AT) is a nonpharmacological 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 twelve 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 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 were increased (p < 0.001); and iii) the t-maps of MCI-1 and MCI-2 were enhanced. Furthermore, an increased functional connectivity 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 nonpharmacological method. (Clinical trial registration number: KCT 0002451, https://cris.nih.go.kr/cris/en/)

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 (∆HbO and ∆HbR) during the resting state, we introduce a secondary (inner) threshold circle using the ∆HbO and ∆HbR magnitudes during the time window of 1 sec where an EEG activity is noticeable. If the trajectory of ∆HbO and ∆HbR touches the resting state threshold circle after passing through the inner circle, this indicates that ∆HbO was increasing and ∆HbR was decreasing (i.e., the start of a hemodynamic response). It takes about 0.5 sec 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 sec. 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 sec. Moreover, the active brain locations (identified using the proposed scheme) were spatially specific when a t-map was made after 10 sec of stimulation. These results demonstrate the possibility of enhancing the classification accuracy for a brain-computer interface with a time window of 1.5 sec using the proposed method.

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.

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.

Functional connectivity is linked to several degenerative brain diseases prevalent in our aging society. Electrical stimulation is used for the clinical treatment and rehabilitation of patients with many cognitive disorders. In this study, the effects of high-definition transcranial direct current stimulation (HD-tDCS) on resting-state brain networks in the human prefrontal cortex were investigated by using functional near-infrared spectroscopy (fNIRS). The intrahemispheric as well as interhemispheric connectivity changes induced by 1 mA HD-tDCS were examined in 15 healthy subjects. Pearson correlation coefficient-based correlation matrices were generated from filtered time series oxyhemoglobin (ΔHbO) signals and converted into binary matrices. Common graph theory metrics were computed to evaluate the network changes. Systematic interhemispheric, intrahemispheric, and intraregional connectivity analyses demonstrated that the stimulation positively affected the resting-state connectivity in the prefrontal cortex. The poststimulation connectivity was increased throughout the prefrontal region, while focal HD-tDCS effects induced an increased rate of connectivity in the stimulated hemisphere. The graph theory metrics clearly distinguished the prestimulation and poststimulation networks for a range of thresholds. The results of this study suggest that HD-tDCS can be used to increase functional connectivity in the prefrontal cortex. The increase in functional connectivity can be explored clinically for neurorehabilitation of patients with degenerative brain diseases.

Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2-7s time window during the a 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.

In this paper, we have classified the initial dips that are detected from the prefrontal and motor cortices using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI). The fNIRS data of mental arithmetic, mental counting, and right-hand finger tapping tasks are acquired from 5 healthy subjects. Vector phase analysis with a threshold circle (as a decision criterion) is used to detect the initial dips. Five different features including signal mean, signal slope, signal minimum value, kurtosis, and skewness in 0∼1, 0∼1.5, 0∼2, and 0∼2.5 sec windows are computed using oxyhemoglobin (HbO) signals. Linear discriminant analysis is used for the classification of the data. The average accuracy of 66.6% is obtained using signal mean and signal minimum value in 0∼2.5 sec window. We used a conventional hemodynamic response to extract the signal mean and signal slope as features in 2∼7 sec window for further validation of our results. LDA-based classification resulted in 73.2% accurate results for conventional hemodynamic response. The results seem significant for BCI using initial dip features.

In this paper, we have improved the classification accuracy of four prefrontal commands decoded using hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) for quadcopter. Mental arithmetic, mental counting, word formation, and mental rotation are used as brain task to generate the commands. The brain signals are decoded simultaneously in a single window using hybrid EEG-fNIRS. We extracted the neuronal and hemodynamic features in 0∼2 sec, 0∼2.25 sec, and 0∼2.5 sec windows. An overlapping window of 0.25 sec is used for online/real-time analysis. Signal peak, signal mean, and signal power are computed as features for EEG. Signal mean, signal slope, signal peak, and minimum negative value are computed as features for fNIRS. We used linear discriminant analysis to classify the features in online scenario. The generated commands are transferred to a quadcopter using Wi-Fi. The quadcopter movements are controlled by the transmitted brain commands. Our results showed that overall system accuracy for fNIRS was increased from 69% to 84 % by combining features with EEG. This enabled more stable control for the quadcopter. Therefore the result seems significant for brain-computer interface applications.

In this paper, we have used a linear combination of three gamma functions to estimate the location of cortical activation during right-hand thumb finger (RHTF) flexion/extension using functional near-infrared spectroscopy (fNIRS). The three gamma functions are used to model the initial dip, conventional hemodynamic response, and undershoot of oxy-hemoglobin signals. The brain signals of five healthy subjects during RHTF flexion/extension task are acquired from the motor cortex. Vector phase analysis with a threshold circle as a decision criterion is used to ensure the presence of initial dips in fNIRS signals. The results show that the brain area around C3 during the RHTF flexion/extension task becomes highly activated for all subjects. Also, the t-map generated for the initial period, i.e., 2.5 sec, is more spatially localized than the t-map drawn for 15 sec period. The result demonstrated that the model obtained using the linear combination of three gamma functions can identify the initial dip brain region for motor activity task.

In this paper, we investigate active brain regions for motor execution and motor imagination tasks after training with a rehabilitation robot. Functional near-infrared spectroscopy (fNIRS) is used to measure the hemodynamic responses in the motor cortices of five subjects. An assistive robot (IMT 2.0, connected to the right hand) is used during the training session to make the subject to reach a target point displayed on a computer screen. During the training, the subjects have to reach the target point in two directions (left and right) using right arm movement. Our intention is to investigate the differences between brain signals generated from left and right movements of the right hand. It was found that the same brain region was activated for both left- and right-directional motions. During the testing, we asked the subjects to imagine the executed movement. We found that although the imagined movement activity is weak but it appears in the same region as that of motor execution during the reach task. The results show that executed and imagined movements can be discriminated using fNIRS. However, for brain-computer interface it is difficult to generate two commands using only one arm movement signals.

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.

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 ∆HbO values within a 2-second moving window. In the case of EEG, two eye-blinks, three eye-blinks, 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 one second 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.

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.

In this study, functional near-infrared spectroscopy (fNIRS) is utilized to measure the hemodynamic responses (HRs) in the visual cortex of 14 subjects (aged 22–34 years) viewing the primary red, green, and blue (RGB) colors displayed on a white screen by a beam projector. The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins (HbO and HbR) in the visual cortex are measured using a 15-source and 15-detector optode configuration. To see whether the activation maps upon RGB-color stimuli can be distinguished or not, the [Formula: see text]-values of individual channels are averaged over 14 subjects. To find the best combination of two features for classification, the HRs of activated channels are averaged over nine trials. The HbO mean, peak, slope, skewness and kurtosis values during 2–7[Formula: see text]s window for a given 10[Formula: see text]s stimulation period are analyzed. Finally, the linear discriminant analysis (LDA) for classifying three classes is applied. Individually, the best classification accuracy obtained with slope-skewness features was 74.07% (Subject 1), whereas the best overall over 14 subjects was 55.29% with peak-skewness combination. Noting that the chance level of 3-class classification is 33.33%, it can be said that RGB colors can be distinguished. The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.

Functional near-infrared spectroscopy (fNIRS), a growing neuroimaging modality, has been utilized over the past few decades to understand the neuronal behavior in the brain. The technique has been used to assess the brain hemodynamics of impaired cohorts as well as able-bodied. Neuroimaging is a critical technique for patients with impaired cognitive or motor behaviors. The portable nature of the fNIRS system is suitable for frequent monitoring of the patients who exhibit impaired brain activity. This study comprehensively reviews brain-impaired patients: The studies involving patient populations and the diseases discussed in more than 10 works are included. Eleven diseases examined in this paper include autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, depressive disorders, anxiety and panic disorder, schizophrenia, mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, stroke, and traumatic brain injury. For each disease, the tasks used for examination, fNIRS variables, and significant findings on the impairment are discussed. The channel configurations and the regions of interest are also outlined. Detecting the occurrence of symptoms at an earlier stage is vital for better rehabilitation and faster recovery. This paper illustrates the usability of fNIRS for early detection of impairment and the usefulness in monitoring the rehabilitation process. Finally, the limitations of the current fNIRS systems (i.e., nonexistence of a standard method and the lack of well-established features for classification) and future research directions are discussed. The authors hope that the findings in this paper would lead to advanced breakthrough discoveries in the fNIRS field in the future.

In this study, we have generated four active commands using hybrid electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for quadcopter control in online environment. Mental arithmetic, left hand clench imagery, and left and right eye-movements are used to navigate the quadcopter. Mental arithmetic task (decoded by fNIRS from the prefrontal cortex) is used to move the quadcopter in forward direction. The left hand clench imagery (decoded by EEG) is used to increase/decrease the height of flight. Signals generated using eye movement in the left/right direction is used for the rotation of the quadcopter. The flight is monitored by the user using the feedback from the frontal camera. The system is unaware of the obstacles in the environment. A fail-safe switch is incorporated in the program using steady-state evoked visual potentials (SSVEP) activity generated at 6 Hz. The fail-safe mechanism stops the forward movement of the quadcopter and the user can adjust the direction of the quadcopter to avoid any collisions with obstacles. The fail-safe trigger can be disabled by focusing on the 6 Hz signal trigger to resume the movement. The experiments conducted on 3 subjects show the possibility of controlling the quadcopter with active commands with increased safety features. The results show the feasibility of increasing commands using hybrid EEG-NIRS for brain-computer interface with applications to quadcopter control.

In this study, we have used functional near-infrared spectroscopy (fNIRS) to measure the hemodynamic response in the visual cortex. We concurrently measure and discriminate the fNIRS signals evoked by three different color stimuli: red, green, and blue (RGB) colors. Seven healthy subjects were asked to perform the RGB color stimuli experiment during a 10 s task and 25 s rest period. Using a continuous-wave fNIRS system, brain signals were acquired concurrently from the visual cortex. Multiclass linear discriminant analysis was utilized to classify RGB stimuli, which resulted in an average classification accuracy of 66.67% across the 7 subjects. A 3∼8 s time window during a 10 s task period provided the best result in classification.

This paper presents a multiple optodes arrangement method in functional near-infrared spectroscopy (fNIRS) for measuring the absolute concentrations of oxy-hemoglobin and deoxy-hemoglobin. To remove physiological noises, short/long-separation detectors are used in the spatially resolved spectroscopy approach. The proposed method is applied to measure the brain activity of five healthy male subjects in mental arithmetic tasks targeting the prefrontal cortex. The obtained results reveal that the proposed method can be used as an alternative approach to the conventional modified Beer-Lambert law. Most of all, the proposed method can remove physiological noises better than the conventional method. Finally, the present work can be extended to a 3D imaging of brain if the entire brain can be covered by neighboring optodes massively and multiple data processing units for real-time computation providing much improved spatial resolution.