Vector-based phase classification of initial dips during word listening using near-infrared spectroscopy

A Novel Approach to Sequential Organ Failure Assessment (SOFA) Using Near-Infrared Spectroscopy in Extracorporeal Membrane Oxygenation (ECMO) Patients

Extracorporeal membrane oxygenation (ECMO) is a medical device that provides temporary external circulation and respiratory support during heart-lung procedures, substituting for heart and lung function to alleviate their burden and allow more time for treatment. This study employs the sequential organ failure assessment (SOFA) to evaluate the severity of illness in ECMO patients and utilizes noninvasive near-infrared spectroscopy (NIRS) to monitor lower limb microcirculation. By extracting and selecting features, blood oxygen information is input into machine learning models for classification and regression analysis. The results indicated that the classification accuracy for disease severity reached 90% for veno-venous (VV-ECMO) and veno-arterial (VA-ECMO) patients, demonstrating the efficacy of combining NIRS with machine learning in clinically distinguishing disease severity. Additionally, the regression analysis yielded excellent performance. These findings underscore the effectiveness of NIRS in assessing disease severity among ECMO patients, offering valuable clinical guidance for optimizing ECMO settings, adjusting cardiovascular medication dosages, and predicting patient prognosis.

An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios

For highly autonomous vehicles, human does not need to operate continuously vehicles. The brain-computer interface system in autonomous vehicles will highly depend on the brain states of passengers rather than those of human drivers. It is a meaningful and vital choice to translate the mental activities of human beings, essentially playing the role of advanced sensors, into safe driving. Quantifying the driving risk cognition of passengers is a basic step toward this end. This study reports the creation of an fNIRS dataset focusing on the prefrontal cortex activity in fourteen types of highly automated driving scenarios. This dataset considers age, sex and driving experience factors and contains the data collected from an 8-channel fNIRS device and the data of driving scenarios. The dataset provides data support for distinguishing the driving risk in highly automated driving scenarios via brain-computer interface systems, and it also provides the possibility of preventing potential hazards in some scenarios, in which risk remains at a high value for an extended period, before hazard occurs.

Virtual reality-based music attention training for acquired brain injury: A protocol for randomized cross-over trial

Attention training is the primary step in the rehabilitation for patients with acquired brain injury (ABI). While active music performance has been reported to aid neural and functional recovery, its efficacy for patients with ABI remains uncertain due to methodological concerns. The purpose of the study is to develop a virtual reality-based music attention training (VR-MAT), which utilizes a visually guided, bilateral drumming in an immersive environment to train attention and executive functions. We also aims to examine the feasibility and effectiveness of the VR-MAT with a small sample size of participants (3-60 months after ABI, N = 20 approximately). Participants will be randomly assigned to either a waitlist control or music group, in which VR-MAT will take place five times weekly over 4 weeks (randomized crossover design). The evaluation of VR-MAT performance will include accuracy and response time in music responses. Neurocognitive outcome measures will be administered to quantify pre-post changes in attention, working memory, and executive functions. Additionally, functional near-infrared spectroscopy will be employed to explore the relationships between musical behavior, neurocognitive function, and neurophysiological responses.

Applying antagonistic activation pattern to the single-trial classification of mental arithmetic

Background

At present, the application of fNIRS in the field of brain-computer interface (BCI) is being a hot topic. By fNIRS-BCI, the brain realizes the control of external devices. A state-of-the-art BCI system has five steps which are cerebral cortex signal acquisition, data pre-processing, feature selection and extraction, feature classification and application interface. Proper feature selection and extraction are crucial to the final fNIRS-BCI effect. This paper proposes a feature selection and extraction method for the mental arithmetic task. Specifically, we modified the antagonistic activation pattern approach and used the combination of antagonistic activation patterns to extract features for enhancement of the classification accuracy with low calculation costs.

Methods

Experiments are conducted on an open-acquisition dataset including fNIRS signals of eight healthy subjects of mental arithmetic (MA) tasks and rest tasks. First, the signals are filtered using band-pass filters to remove noise. Second, channels are selected by prior knowledge about antagonistic activation patterns. We used cerebral blood volume (CBV) and cerebral oxygen exchange (COE) of selected each channel to build novel attributes. Finally, we proposed three groups of attributes which are CBV, COE and CBV + COE. Based on attributes generated by the proposed method, we calculated temporal statistical measures (average, variance, maximum, minimum and slope). Any two of five statistical measures were combined as feature sets.

Main results

With the LDA, QDA, and SVM classifiers, the proposed method obtained higher classification accuracies the basic control method. The maximum classification accuracies achieved by the proposed method are 67.45 ± 14.56% with LDA classifier, 89.73 ± 5.71% with QDA classifier, and 87.04 ± 6.88% with SVM classifier. The novel method reduced the running time by 3.75 times compared with the method incorporating all channels into the feature set. Therefore, the novel method reduces the computational costs while maintaining high classification accuracy. The results are validated by another open-access dataset including MA and rest tasks of 29 healthy subjects.

Vector-based analysis of cortical activity associated with dumbbell exercise using functional near-infrared spectroscopy

The mechanisms via which the brain and muscles work together remain poorly understood. The use of vector-based fNIRS, to propose a new metric and imaging method to understand neural activation during dumbbell-lifting exercises. This method can simultaneously measure oxyhemoglobin (oxyHb) and deoxyHb levels so that the angle k: Arctan (deoxyHb/oxyHb) represents the degree of oxygen exchange in the brain and can be used to quantify the distribution of oxygen consumption. The amplitude L of the vector reflects the intensity of the response caused by the amount of change in Hb. This study used vector-based fNIRS to simultaneously measure the left primary motor cortex (left M1), multiple peripheral regions, and the right biceps brachii muscle. The subjects were seven healthy adults. The task was a dumbbell-lifting exercise involving flexion and extension of the elbow joints of both arms. Dumbbell weights of 0 (no dumbbell), 4.5, and 9.5 kg were used. During dumbbell exercise, oxygen exchange increased in the left M1, indicating increased local oxygen consumption. Around the left M1, the cerebral oxygen exchange decreased, and oxygen supply increased without cerebral oxygen consumption. The spatial agreement between the maximum value of oxygen exchange k and L during the task was <20%. Therefore, the dumbbell-lifting exercise task study reported here supported the hypothesis that cerebral oxygen consumption associated with neural activation does not coincide with the distribution of cerebral oxygen supply. The relationship between the brain oxygen supply from the site of increased oxygen exchange in the brain and its surrounding areas can be quantified using the vector method fNIRS.

Functional quantification of oral motor cortex at rest and during tasks using activity phase ratio: A zero-setting vector functional near-infrared spectroscopy study

Oral frailty associated with oral hypokinesia may cause dementia. Functional near-infrared spectroscopy (fNIRS) can be used while the participants are in seating position with few restrictions. Thus, it is useful for assessing brain function, particularly oral motor activity. However, methods for identifying oral motor cortex (OMC) activation via the scalp have not been established. The current study aimed to detect OMC activation, an indicator of activity phase ratio (APR), which reflects increased oxygen consumption (0 &lt; [deoxyhemoglobin (ΔDeoxyHb) or 0 &lt; {[ΔDeoxyHb- oxyhemoglobin (ΔOxyHb)/√2]}, via fNIRS to accurately identify local brain activity. The APR, calculated via zero-set vector analysis, is a novel index for quantifying brain function both temporally and spatially at rest and during tasks. In total, 14 healthy participants performed bite tasks for 3 s per side for 10 times while in the sitting position. Then, time-series data on concentration changes in ΔOxyHb and ΔDeoxyHb were obtained via fNIRS. The anatomical location of the OMC was determined using a pooled data set of three-dimensional magnetic resonance images collected in advance from 40 healthy adults. In the zero-set vector analysis, the average change in ΔOxyHb and ΔDeoxyHb concentrations was utilized to calculate the APR percentage in 140 trials. The significant regions (z-score of ≥2.0) of the APR and ΔOxyHb in the task were compared. During the bite task, the APR significantly increased within the estimated OMC region (56–84 mm lateral to Cz and 4–20 mm anterior to Cz) in both the right and left hemispheres. By contrast, the ΔOxyHb concentrations increased on the bite side alone beyond the OMC region. The mean APR at rest for 2 s before the task showed 59.5%–62.2% in the left and right OMCs. The average APR for 3 s during the task showed 75.3% for the left OMC and 75.7% for the right OMC during the left bite task, and 65.9% for the left OMC and 80.9% for the right OMC during the right bite task. Interestingly, the average increase in APR for the left and right OMCs for the left bite task and the right bite task was 13.9% and 13.7%, respectively, showing almost a close match. The time course of the APR was more limited to the bite task segment than that of ΔOxyHb or ΔDexyHb concentration, and it increased in the OMC. Hence, the APR can quantitatively monitor both the resting and active states of the OMC in the left and right hemispheres. Using the zero-set vector-based fNIRS, the APR can be a valid indicator of oral motor function and bite force.

Basic Examination of Haemoglobin Phase of Oxygenation and Deoxygenation in Resting State and Task Periods in Adults Using fNIRS

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used to measure the relative changes in concentrations of oxygenated haemoglobin (oxy-Hb) and deoxygenated haemoglobin (deoxy-Hb) in the cerebral cortex. While most previous studies using fNIRS have relied only on a single oxy-Hb or deoxy-Hb parameter to infer about neural activation, the phase difference between the oxy- and deoxy-Hb signals (haemoglobin phase of oxygenation and deoxygenation: hPod) has been reported to be an important biomarker for analysing haemodynamic characteristics of the brain in infants. In this study, we examined the basic characteristics of adult hPod to develop a new analysis method to detect more sensitive signals that reflect neural activation in adults using fNIRS. We measured the hPod of 12 healthy adults in the frontal and occipital cortex during rest and upon exposure to visual stimuli and the verbal working memory (WM) task. We found that the average hPod values during the entire measurement period ranged between π and 1.5π rad in all conditions. This result indicates that the phase differences in adults were generally close to a stable antiphase pattern (hPod values around π), regardless of the presence or absence of tasks and stimuli. However, when dynamic changes in hPod values were analysed, significant differences between the resting state and WM tasks were observed during activation period in the frontal and occipital regions. These results suggest that the analysis of dynamic hPod change is useful for detecting a subtle activation for cognitive tasks.

fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls

In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two subjects, including 16 patients clinically diagnosed with MDD and 16 healthy controls (HCs), participated in the study. Brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) responses were recorded using a 22-channel continuous-wave fNIRS device while the subjects performed the emotional sound test. This study evaluated the difference between MDD patients and HCs using a variety of methods. In a comparison of the Pearson correlation coefficients between the HbO/HbR responses of each fNIRS channel and four scores, MDD patients and HCs had significantly different Athens Insomnia Scale (AIS) scores. By quantitative evaluation of the functional association, we found that MDD patients had aberrant functional connectivity compared with HCs. Furthermore, we concluded that compared with HCs, there were marked abnormalities in blood oxygen in the bilateral ventrolateral prefrontal cortex (VLPFC) and bilateral dorsolateral prefrontal cortex (DLPFC). Four statistical-based features extracted from HbO signals and four vector-based features from both HbO and HbR served as inputs to four simple neural networks (multilayer neural network (MNN), feedforward neural network (FNN), cascade forward neural network (CFNN) and recurrent neural network (RNN)). Through an analysis of combinations of different features, the combination of 4 common features (mean, STD, area under the receiver operating characteristic curve (AUC) and slope) yielded the highest classification accuracy of 89.74% for fear emotion. The combination of four novel feature (CBV, COE, |L | and K) resulted in a classification accuracy of 99.94% for fear emotion. The top 10 common and novel features were selected by the ReliefF feature selection algorithm, resulting in classification accuracies of 83.52% and 91.99%, respectively. This study identified the AUC and angle K as specific neuromarkers for predicting MDD across specific depression-related regions of the prefrontal cortex (PFC). These findings suggest that the fNIRS measurement of the PFC may serve as a supplementary test in routine clinical practice to further support a diagnosis of MDD.

Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface

A passive brain-computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects' data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.

Relationship Between Decrease of Oxygenation During Incremental Exercise and Partial Pressure End-Tidal Carbon Dioxide: Near-Infrared Spectroscopy Vector Analysis

A previous study considered that a decrease in cerebral oxyhemoglobin (O2Hb) immediately before maximal exercise during incremental exercise is related to cerebral blood flow (CBF) and partial pressure end-tidal carbon dioxide (PETCO2). This study aimed to investigate the relationship between O2Hb, PETCO2, and the estimated value of cerebral blood volume (CBV) with cerebral oxygen exchange (COE) by using vector analysis. Twenty-four healthy young men participated in this study. They performed the incremental exercise (20 W/min) after a 4-min rest and warm-up. The O2Hb and deoxyhemoglobin (HHb) in the prefrontal cortex (PFC) were measured using near-infrared spectroscopy (NIRS). The PETCO2 was measured using a gas analyzer. The O2Hb, HHb, and PETCO2 were calculated as the amount of change (ΔO2Hb, ΔHHb, and ΔPETCO2) from an average 4-min rest. Changes in the CBV (ΔCBV) and COE (ΔCOE) were estimated using NIRS vector analysis. Moreover, the respiratory compensation point (RCP), which relates to the O2Hb decline, was detected. The Pearson correlation coefficient was used to establish the relationships among ΔO2Hb, ΔPETCO2, ΔCBV, and ΔCOE from the RCP to maximal exercise. The ΔPETCO2 did not significantly correlate with the ΔO2Hb (r = 0.03, p = 0.88), ΔCOE (r = -0.19, p = 0.36), and ΔCBV (r = -0.21, p = 0.31). These results showed that changes in the ΔPETCO2 from the RCP to maximal exercise were not related to changes in the ΔO2Hb, ΔCOE, and ΔCBV. Therefore, we suggested that the decrease of O2Hb immediately before maximal exercise during incremental exercise may be related to cerebral oxygen metabolism by neural activity increase, not decrease of CBF by the PETCO2.

Effect of stimulation duration to the existence of initial dip

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.

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance.

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.

Approach to optimize 3-dimensional brain functional activation image with high resolution: a study on functional near-infrared spectroscopy

In this study, 3-dimensional (3-D) enhanced brain-function-map generation and estimation methodology is presented. Optical signals were modelled in the form of numerical optimization problem to infer the best existing waveform of canonical hemodynamic response function. Inter-channel activity patterns were also estimated. The estimation of activation of inter-channel gap depends on the minimization of generalized cross-validation. 3-D brain activation maps were produced through inverse discrete cosine transform. The proposed algorithm acquired significant results for 3-D functional maps with high resolution, in comparison with that of 2-D functional t-maps. A comprehensive analysis by exhibiting images corresponding to several layers has also been appended.

Multi-distance frequency-domain optical measurements of coherent cerebral hemodynamics

We report non-invasive, bilateral optical measurements on the forehead of five healthy human subjects, of 0.1 Hz oscillatory hemodynamics elicited either by cyclic inflation of pneumatic thigh cuffs, or by paced breathing. Optical intensity and the phase of photon-density waves were collected with frequency-domain near-infrared spectroscopy at seven source-detector distances (11-40 mm). Coherent hemodynamic oscillations are represented by phasors of oxyhemoglobin (O) and deoxyhemoglobin (D) concentrations, and by the vector D/O that represents the amplitude ratio and phase difference of D and O. We found that, on average, the amplitude ratio (|D/O|) and the phase difference (∠(D/O)) obtained with single-distance intensity at 11-40 mm increase from 0.1 and -330°, to 0.2 and -200°, respectively. Single-distance phase and the intensity slope featured a weaker dependence on source-detector separation, and yielded |D/O| and ∠(D/O) values of about 0.5 and -200°, respectively, at distances greater than 20 mm. The key findings are: (1) single-distance phase and intensity slope are sensitive to deeper tissue compared to single-distance intensity; (2) deeper tissue hemodynamic oscillations, which more closely represent the brain, feature D and O phasors that are consistent with a greater relative flow-to-volume contributions in brain tissue compared to extracerebral, superficial tissue.

Initial-Dip Existence and Estimation in Relation to DPF and Data Drift

Early de-oxygenation (initial dip) is an indicator of the primal cortical activity source in functional neuro-imaging. In this study, initial dip's existence and its estimation in relation to the differential pathlength factor (DPF) and data drift were investigated in detail. An efficient algorithm for estimation of drift in fNIRS data is proposed. The results favor the shifting of the fNIRS signal to a transformed coordinate system to infer correct information. Additionally, in this study, the effect of the DPF on initial dip was comprehensively analyzed. Four different cases of initial dip existence were treated, and the resultant characteristics of the hemodynamic response function (HRF) for DPF variation corresponding to particular near-infrared (NIR) wavelengths were summarized. A unique neuro-activation model and its iterative optimization solution that can estimate drift in fNIRS data and determine the best possible fit of HRF with free parameters were developed and herein proposed. The results were verified on simulated data sets. The algorithm is applied to free available datasets in addition to six healthy subjects those were experimented using fNIRS and observations and analysis regarding shape of HRF were summarized as well. A comparison with standard GLM is also discussed and effects of activity strength parameters have also been analyzed.

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.

High Oxygen Exchange to Music Indicates Auditory Distractibility in Acquired Brain Injury: An fNIRS Study with a Vector-Based Phase Analysis

Attention deficits due to auditory distractibility are pervasive among patients with acquired brain injury (ABI). It remains unclear, however, whether attention deficits following ABI specific to auditory modality are associated with altered haemodynamic responses. Here, we examined cerebral haemodynamic changes using functional near-infrared spectroscopy combined with a topological vector-based analysis method. A total of thirty-seven participants (22 healthy adults, 15 patients with ABI) performed a melodic contour identification task (CIT) that simulates auditory distractibility. Findings demonstrated that the melodic CIT was able to detect auditory distractibility in patients with ABI. The rate-corrected score showed that the ABI group performed significantly worse than the non-ABI group in both CIT1 (target contour identification against environmental sounds) and CIT2 (target contour identification against target-like distraction). Phase-associated response intensity during the CITs was greater in the ABI group than in the non-ABI group. Moreover, there existed a significant interaction effect in the left dorsolateral prefrontal cortex (DLPFC) during CIT1 and CIT2. These findings indicated that stronger hemodynamic responses involving oxygen exchange in the left DLPFC can serve as a biomarker for evaluating and monitoring auditory distractibility, which could potentially lead to the discovery of the underlying mechanism that causes auditory attention deficits in patients with ABI.

Perspective: Prospects of non-invasive sensing of the human brain with diffuse optical imaging

Since the initial demonstration of near-infrared spectroscopy (NIRS) for noninvasive measurements of brain perfusion and metabolism in the 1970s, and its application to functional brain studies (fNIRS) in the 1990s, the field of noninvasive optical studies of the brain has been continuously growing. Technological developments, data analysis advances, and novel areas of application keep advancing the field. In this article, we provide a view of the state of the field of cerebral NIRS, starting with a brief historical introduction and a description of the information content of the NIRS signal. We argue that NIRS and fNIRS studies should always report data of both oxy- and deoxyhemoglobin concentrations in brain tissue, as they complement each other to provide more complete functional and physiological information, and may help identify different types of confounds. One significant challenge is the assessment of absolute tissue properties, be them optical or physiological, so that relative measurements account for the vast majority of NIRS and fNIRS applications. However, even relative measurements of hemodynamics or metabolic changes face the major problem of a potential contamination from extracerebral tissue layers. Accounting for extracerebral contributions to fNIRS signals is one of the most critical barriers in the field. We present some of the approaches that were proposed to tackle this challenge in the study of cerebral hemodynamics and functional connectivity. Finally, we critically compare fNIRS and functional magnetic resonance imaging (fMRI) by relating their measurements in terms of signal and noise, and by commenting on their complementarity.

The meaning of "coherent" and its quantification in coherent hemodynamics spectroscopy

We have recently introduced a new technique, coherent hemodynamics spectroscopy (CHS), which aims at characterizing a specific kind of tissue hemodynamics that feature a high level of covariation with a given physiological quantity. In this study, we carry out a detailed analysis of the significance of coherence and phase synchronization between oscillations of arterial blood pressure (ABP) and total hemoglobin concentration ([Hbt]), measured with near-infrared spectroscopy (NIRS) during a typical protocol for CHS, based on a cyclic thigh cuff occlusion and release. Even though CHS is based on a linear time invariant model between ABP (input) and NIRS measurands (outputs), for practical reasons in a typical CHS protocol, we induce finite "groups" of ABP oscillations, in which each group is characterized by a different frequency. For this reason, ABP (input) and NIRS measurands (output) are not stationary processes, and we have used wavelet coherence and phase synchronization index (PSI), as a metric of coherence and phase synchronization, respectively. PSI was calculated by using both the wavelet cross spectrum and the Hilbert transform. We have also used linear coherence (which requires stationary process) for comparison with wavelet coherence. The method of surrogate data is used to find critical values for the significance of covariation between ABP and [Hbt]. Because we have found similar critical values for wavelet coherence and PSI by using five of the most used methods of surrogate data, we propose to use the data-independent Gaussian random numbers (GRNs), for CHS. By using wavelet coherence and wavelet cross spectrum, and GRNs as surrogate data, we have found the same results for the significance of coherence and phase synchronization between ABP and [Hbt]: on a total set of 20 periods of cuff oscillations, we have found 17 coherent oscillations and 17 phase synchronous oscillations. Phase synchronization assessed with Hilbert transform yielded similar results with 14 phase synchronous oscillations. Linear coherence and wavelet coherence overall yielded similar number of significant values. We discuss possible reasons for this result. Despite the similarity of linear and wavelet coherence, we argue that wavelet coherence is preferable, especially if one wants to use baseline spontaneous oscillations, in which phase locking and coherence between signals might be only temporary.

Existence of Initial Dip for BCI: An Illusion or Reality

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.

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.

Hemoglobin state-flux: A finite-state model representation of the hemoglobin signal for evaluation of the resting state and the influence of disease

SUMMARY

In this report we introduce a weak-model approach for examination of the intrinsic time-varying properties of the hemoglobin signal, with the aim of advancing the application of functional near infrared spectroscopy (fNIRS) for the detection of breast cancer, among other potential uses. The developed methodology integrates concepts from stochastic network theory with known modulatory features of the vascular bed, and in doing so provides access to a previously unrecognized dense feature space that is shown to have promising diagnostic potential. Notable features of the methodology include access to this information solely from measures acquired in the resting state, and analysis of these by treating the various components of the hemoglobin (Hb) signal as a co-varying interacting system.

APPROACH

The principal data-transform kernel projects Hb state-space trajectories onto a coordinate system that constitutes a finite-state representation of covariations among the principal elements of the Hb signal (i.e., its oxygenated (ΔoxyHb) and deoxygenated (ΔdeoxyHb) forms and the associated dependent quantities: total hemoglobin (ΔtotalHb = ΔoxyHb + ΔdeoxyHb), hemoglobin oxygen saturation (ΔHbO2Sat = 100Δ(oxyHb/totalHb)), and tissue-hemoglobin oxygen exchange (ΔHbO2Exc = ΔdeoxyHb-ΔoxyHb)). The resulting ten-state representation treats the evolution of this signal as a one-space, spatiotemporal network that undergoes transitions from one state to another. States of the network are defined by the algebraic signs of the amplitudes of the time-varying components of the Hb signal relative to their temporal mean values. This assignment produces several classes of coefficient arrays, most with a dimension of 10×10.

BIOLOGICAL MOTIVATION

Motivating our approach is the understanding that effector mechanisms that modulate blood delivery to tissue operate on macroscopic scales, in a spatially and temporally varying manner. Also recognized is that this behavior is sensitive to nonlinear actions of these effectors, which include the binding properties of hemoglobin. Accessible phenomenology includes measures of the kinetics and probabilities of network dynamics, which we treat as surrogates for the actions of feedback mechanisms that modulate tissue-vascular coupling.

FINDINGS

Qualitative and quantitative features of this space, and their potential to serve as markers of disease, have been explored by examining continuous-wave fNIRS 3D tomographic time series obtained from the breasts of women who do and do not have breast cancer. Inspection of the coefficient arrays reveals that they are governed predominantly by first-order rate processes, and that each array class exhibits preferred structure that is mainly independent of the others. Discussed are strategies that may serve to extend evaluation of the accessible feature space and how the character of this information holds potential for development of novel clinical and preclinical uses.

Spatial variation in the hemoglobin phase of oxygenation and deoxygenation in the developing cortex of infants

Spontaneous low-frequency oscillatory changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) are observed using functional near-infrared spectroscopy (fNIRS). A previous study showed that the time-averaged phase difference between oxy-Hb and deoxy-Hb changes, referred to as hemoglobin phase of oxygenation and deoxygenation (hPod), is sensitive to the development of the cortex. We examined phase-locking index of hPod, referred to as [Formula: see text], in addition to hPod, in neonates and 3- and 6-month-old infants using the 94-channel fNIRS data, which covered large lateral regions of the cortex. The results showed that (1) developmental changes in hPod exhibited spatial dependency; (2) [Formula: see text] increased between the neonate group and 3-month-old infant group over the posterior, but not anterior, regions of the cortex; and (3) the cortical regions of each age group were clustered in several domains with specific characteristics of hPod and [Formula: see text]. This study indicates that the neonatal cortex is composed of regions with specific characteristics of hPod and [Formula: see text], and drastic changes occur between the neonatal period and 3 months of age. This study suggests that hPod and [Formula: see text] are sensitive to the cortical region-specific development of the circulatory, blood flow, metabolic, and neurovascular functions in young infants.

Non-neuronal evoked and spontaneous hemodynamic changes in the anterior temporal region of the human head may lead to misinterpretations of functional near-infrared spectroscopy signals

Several functional near-infrared spectroscopy (fNIRS) studies report their findings based on changes of a single chromophore, usually concentration changes of oxygenated hemoglobin ([[Formula: see text]]) or deoxygenated hemoglobin (HHb). However, influence of physiological actions may differ depending on which element is considered and the assumption that the chosen measure correlates with the neural response of interest might not hold. By assessing the correlation between [[Formula: see text]] and [HHb] in task-evoked activity as well as resting-state data, we identified a spatial dependency of non-neuronal hemodynamic changes in the anterior temporal region of the human head. Our findings support the importance of reporting and discussing fNIRS outcomes obtained with both chromophores ([[Formula: see text]] and [HHb]), in particular, for studies concerning the anterior temporal region of the human head. This practice should help to achieve a physiologically correct interpretation of the results when no measurements with short-distance channels are available while employing continuous-wave fNIRS systems.

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.

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.

Reduction of Delay in Detecting Initial Dips from Functional Near-Infrared Spectroscopy Signals Using Vector-Based Phase Analysis

In this paper, we present a systematic method to reduce the time lag in detecting initial dips using a vector-based phase diagram and an autoregressive moving average with exogenous signals (ARMAX) model-based [Formula: see text]-step-ahead prediction algorithm. With functional near-infrared spectroscopy (fNIRS), signals related to mental arithmetic and right-hand clenching are acquired from the prefrontal and left primary motor cortices, respectively. The interrelationship between oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin and cerebral oxygen exchange are related to initial dips. Specifically, a threshold value from the resting state hemodynamics is incorporated, as a decision criterion, into the vector-based phase diagram to determine the occurrence of initial dips. To further reduce the time lag, a [Formula: see text]-step-ahead prediction method is applied to predict the occurrence of the dips. A combination of the threshold criterion and the prediction method resulted in the delay time of about 0.9[Formula: see text]s. The results demonstrate that rapid detection of initial dip is possible and therefore can be used for real-time brain–computer interfacing.

Event-related changes of the prefrontal cortex oxygen delivery and metabolism during driving measured by hyperspectral fNIRS

Recent technological advancements in optical spectroscopy allow for the construction of hyperspectral (broadband) portable tissue oximeters. In a series of our recent papers we have shown that hyperspectral NIRS (hNIRS) has similar or better capabilities in the absolute tissue oximetry as frequency-domain NIRS, and that hNIRS is also very efficient in measuring temporal changes in tissue hemoglobin concentration and oxygenation. In this paper, we extend the application of hNIRS to the measurement of event-related hemodynamic and metabolic functional cerebral responses during simulated driving. In order to check if hNIRS can detect event-related changes in the brain, we measured the concentration changes of oxygenated (HbO2) and deoxygenated (HHb) hemoglobin and of the oxidized state of cytochrome c oxidase, on the right and left prefrontal cortices (PFC) simultaneously during simulated driving on sixteen healthy right-handed participants (aged between 22-32). We used our in-house hNIRS system based on a portable spectrometer with cooled CCD detector and a driving simulator with a fully functional steering wheel and foot pedals. Each participant performed different driving tasks and participants were distracted during some driving conditions by asking general knowledge true/false questions. Our findings suggest that more complex driving tasks (non-distracted) deactivate PFC while distractions during driving significantly activate PFC, which is in agreement with previous fMRI results. Also, we found the changes in the redox state of the cytochrome C oxidase to be very consistent with those in the concentrations of HbO2 and HHb. Overall our findings suggest that in addition to the suitability of absolute tissue oximetry, hyperspectral NIRS may also offer advantages in functional brain imaging. In particular, it can be used to measure the metabolic functional brain activity during actual driving.

Greater Activity in the Frontal Cortex on Left Curves: A Vector-Based fNIRS Study of Left and Right Curve Driving

OBJECTIVES

In the brain, the mechanisms of attention to the left and the right are known to be different. It is possible that brain activity when driving also differs with different horizontal road alignments (left or right curves), but little is known about this. We found driver brain activity to be different when driving on left and right curves, in an experiment using a large-scale driving simulator and functional near-infrared spectroscopy (fNIRS).

RESEARCH DESIGN AND METHODS

The participants were fifteen healthy adults. We created a course simulating an expressway, comprising straight line driving and gentle left and right curves, and monitored the participants under driving conditions, in which they drove at a constant speed of 100 km/h, and under non-driving conditions, in which they simply watched the screen (visual task). Changes in hemoglobin concentrations were monitored at 48 channels including the prefrontal cortex, the premotor cortex, the primary motor cortex and the parietal cortex. From orthogonal vectors of changes in deoxyhemoglobin and changes in oxyhemoglobin, we calculated changes in cerebral oxygen exchange, reflecting neural activity, and statistically compared the resulting values from the right and left curve sections.

RESULTS

Under driving conditions, there were no sites where cerebral oxygen exchange increased significantly more during right curves than during left curves (p > 0.05), but cerebral oxygen exchange increased significantly more during left curves (p < 0.05) in the right premotor cortex, the right frontal eye field and the bilateral prefrontal cortex. Under non-driving conditions, increases were significantly greater during left curves (p < 0.05) only in the right frontal eye field.

CONCLUSIONS

Left curve driving was thus found to require more brain activity at multiple sites, suggesting that left curve driving may require more visual attention than right curve driving. The right frontal eye field was activated under both driving and non-driving conditions.

A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching

OBJECTIVE

In order to increase the number of states classified by a brain-computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching.

APPROACH

The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs).

MAIN RESULTS

In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG-fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89% ± 2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature.

SIGNIFICANCE

Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.

fNIRS-based brain-computer interfaces: a review

A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

Feasibility of near-infrared spectroscopic tomography for intraoperative functional cerebral monitoring: a primate study

OBJECTIVE

The wide-ranging manipulations to the cardiovascular system that frequently occur during cardiac surgery can expose the brain to variations in its blood supply that could prove deleterious. As a first step to developing a resource suitable for monitoring such changes, we detected the hemodynamic events induced in the brain of a primate model, using high-density near-infrared spectroscopy combined with tomographic reconstruction methods and validated the findings using established radiologic and histologic techniques.

METHODS

Continuous monitoring of the relative changes in the components of the cerebral hemoglobin signal was performed using high-density near-infrared spectroscopy (270 source-detector channel array) in anesthetized bonnet macaques with the brain exposed to induced ischemia and other acute events. A comparative analysis (exact binomial test) applied to reconstructed 3-dimensional images before and after the events and between cerebral hemispheres, combined with postprocedure magnetic resonance imaging, and postmortem histopathologic examination of the macaques' brains was performed to document and validate the spatial features revealed by the optical findings.

RESULTS

Relative changes in the measured and calculated components of the hemoglobin signal, in response to the performed manipulations, revealed substantial concurrence among the reconstructed 3-dimensional images, magnetic resonance imaging of the macaques' brains, and postmortem histopathologic examination findings. Concurrence was seen when the manipulated hemoglobin concentration and associated oxygenation levels were either increased or decreased, and whether they were bilateral or restricted to a specified hemisphere.

CONCLUSIONS

Continuous near-infrared spectroscopy tomography has been shown to accurately capture and localize cerebral ischemia, vasodilatation, and hemorrhage in primates in real time. These findings are directly applicable to clinical intraoperative functional cerebral monitoring.

Increased oxygen load in the prefrontal cortex from mouth breathing: a vector-based near-infrared spectroscopy study

Individuals who habitually breathe through the mouth are more likely than nasal breathers to have sleep disorders and attention deficit hyperactive disorder. We hypothesized that brain hemodynamic responses in the prefrontal cortex might be different for mouth and nasal breathing. To test this hypothesis, we measured changes in oxyhemoglobin and deoxyhemoglobin in the prefrontal cortex during mouth breathing and nasal breathing in healthy adults (n=9) using vector-based near-infrared spectroscopy. The angle k, calculated from changes in oxyhemoglobin and deoxyhemoglobin and indicating the degree of oxygen exchange, was significantly higher during mouth breathing (P<0.05), indicating an increased oxygen load. Mouth breathing also caused a significant increase in deoxyhemoglobin, but oxyhemoglobin did not increase. This difference in oxygen load in the brain arising from different breathing routes can be evaluated quantitatively using vector-based near-infrared spectroscopy. Phase responses could help to provide an earlier and more reliable diagnosis of a patient’s habitual breathing route than a patient interview.

Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study

Traffic accidents occur more frequently during deceleration than during acceleration. However, little is known about the relationship between brain activation and vehicle acceleration because it has been difficult to measure the brain activation of drivers while they drive. In this study, we measured brain activation during actual driving using vector-based functional near-infrared spectroscopy. Subjects decelerated from 100 to 50 km/h (speed reduction task) and accelerated from 50 to 100 km/h (speed increase task) while driving on an expressway, in the daytime and at night. We examined correlations between average vehicle acceleration in each task and five hemodynamic indices: changes in oxygenated hemoglobin (ΔoxyHb), deoxygenated hemoglobin (ΔdeoxyHb), cerebral blood volume (ΔCBV), and cerebral oxygen exchange (ΔCOE); and the phase angle k (degrees) derived from the other hemoglobin (Hb) indices. ΔoxyHb and ΔCBV reflect changes in cerebral blood flow, whereas ΔdeoxyHb, ΔCOE, and k are related to variations in cerebral oxygen metabolism. Most of the resulting correlations with specific brain sites, for all the indices, appeared during deceleration rather than during acceleration. Faster deceleration resulted in greater increases in ΔdeoxyHb, ΔCOE, and k in the prefrontal cortex (r < −0.5, p < 0.01), in particular, in the frontal eye field, and at night, it also resulted in greater decreases in ΔoxyHb and ΔCBV in the prefrontal cortex and in the parietal lobe (r > 0.4, p < 0.01), suggesting oxygen metabolism associated with transient ischemic changes. Our results suggest that vehicle deceleration requires more brain activation, focused in the prefrontal cortex, than does acceleration. From the standpoint of the indices used, we found that simultaneous analysis of multiple hemodynamic indices was able to detect not only the blood flow components of hemodynamic responses, but also more localized frontal lobe activation involving oxygen metabolism.

Functional brain imaging using near-infrared spectroscopy during actual driving on an expressway

The prefrontal cortex is considered to have a significant effect on driving behavior, but little is known about prefrontal cortex function in actual road driving. Driving simulation experiments are not the same, because the subject is in a stationary state, and the results may be different. Functional near-infrared spectroscopy (fNIRS) is advantageous in that it can measure cerebral hemodynamic responses in a person driving an actual vehicle. We mounted fNIRS equipment in a vehicle to evaluate brain functions related to various actual driving operations while the subjects drove on a section of an expressway that was not yet open to the public. Measurements were recorded while parked, and during acceleration, constant velocity driving (CVD), deceleration, and U-turns, in the daytime and at night. Changes in cerebral oxygen exchange (ΔCOE) and cerebral blood volume were calculated and imaged for each part of the task. Responses from the prefrontal cortex and the parietal cortex were highly reproducible in the daytime and nighttime experiments. Significant increases in ΔCOE were observed in the frontal eye field (FEF), which has not been mentioned much in previous simulation experiments. In particular, significant activation was detected during acceleration in the right FEF, and during deceleration in the left FEF. Weaker responses during CVD suggest that FEF function was increased during changes in vehicle speed. As the FEF contributes to control of eye movement in three-dimensional space, FEF activation may be important in actual road driving. fNIRS is a powerful technique for investigating brain activation outdoors, and it proved to be sufficiently robust for use in an actual highway driving experiment in the field of intelligent transport systems (ITS).