Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water

Altered cerebrovascular-CSF coupling in Alzheimer's Disease measured by functional near-infrared spectroscopy

In-vivo microscopical studies indicate that brain cerebrospinal fluid (CSF) transport driven by blood vessel pulsations is reduced in the early stages of Alzheimer's disease (AD). We hypothesized that the coupling pattern between cerebrovascular pulsations and CSF is altered in AD, and this can be measured using multi-wavelength functional near-infrared spectroscopy (fNIRS). To study this, we quantified simultaneously cerebral hemo- and CSF hydrodynamics in early AD patients and age-matched healthy controls. Physiological pulsations were analysed in the vasomotor very low frequency (VLF 0.008-0.1 Hz), respiratory (Resp. 0.1-0.6 Hz), and cardiac (Card. 0.6-5 Hz) bands. A sliding time window cross-correlation approach was used to estimate the temporal stability of the cerebrovascular-CSF coupling. We investigated how the lag time series variation of the coupling differs between AD patients and control. The couplings involving deoxyhemoglobin (HbR) and CSF water, along with their first derivative, in the cardiac band demonstrated significant difference between AD patients and controls. Furthermore, the lag time series variation of HbR-CSF in the cardiac band provided a significant relationship, p-value = 0.04 and r2 = 0.16, with the mini-mental state exam (MMSE) score. In conclusion, the coupling pattern between hemodynamics and CSF is reduced in AD and it correlates with MMSE score.

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

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

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.

Reliability of fNIRS for noninvasive monitoring of brain function and emotion in sheep

The aim of this work was to critically assess if functional near infrared spectroscopy (fNIRS) can be profitably used as a tool for noninvasive recording of brain functions and emotions in sheep. We considered an experimental design including advances in instrumentation (customized wireless multi-distance fNIRS system), more accurate physical modelling (two-layer model for photon diffusion and 3D Monte Carlo simulations), support from neuroanatomical tools (positioning of the fNIRS probe by MRI and DTI data of the very same animals), and rigorous protocols (motor task, startling test) for testing the behavioral response of freely moving sheep. Almost no hemodynamic response was found in the extra-cerebral region in both the motor task and the startling test. In the motor task, as expected we found a canonical hemodynamic response in the cerebral region when sheep were walking. In the startling test, the measured hemodynamic response in the cerebral region was mainly from movement. Overall, these results indicate that with the current setup and probe positioning we are primarily measuring the motor area of the sheep brain, and not probing the too deeply located cortical areas related to processing of emotions.

A Portable and Wireless Multi-Channel Acquisition System for Physiological Signal Measurements

We propose a portable and wireless acquisition system to help consumers or users register important physiological signals. The acquisition system mainly consists of a portable device, a graphic user interface (GUI), and an application program for displaying the signals on a notebook (NB) computer or a smart device. Essential characteristics of the portable device include eight measuring channels, a powerful microcontroller unit, a lithium battery, Bluetooth 3.0 data transmission, and a built-in 2 GB flash memory. In addition, the signals that are measured can be displayed on a tablet, a smart phone, or a notebook computer concurrently. Additionally, the proposed system provides extra power supply sources of ±3 V for the usage of external circuits. On the other hand, consumers or users can design their own sensing circuits and combine them with this system to carry out ubiquitous physiological studies. Four major advantages in the proposed system are the capability of combining it with a NB computer or a smart phone to display the signals being measured in real time, the superior mobility due to its own independent power system, flash memory, and good expandability. Briefly, this acquisition system offers consumers or users a convenient and portable studying tool to measure dynamic vital signals of interest in psychological and physiological research fields.

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

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

Adaptive filtering of physiological noises in fNIRS data

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

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.

Evaluation of light detector surface area for functional Near Infrared Spectroscopy

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neuroimaging technique that utilizes near infrared light to detect cortical concentration changes of oxy-hemoglobin and deoxy-hemoglobin non-invasively. Using light sources and detectors over the scalp, multi-wavelength light intensities are recorded as time series and converted to concentration changes of hemoglobin via modified Beer-Lambert law. Here, we describe a potential source for systematic error in the calculation of hemoglobin changes and light intensity measurements. Previous system characterization and analysis studies looked into various fNIRS parameters such as type of light source, number and selection of wavelengths, distance between light source and detector. In this study, we have analyzed the contribution of light detector surface area to the overall outcome. Results from Monte Carlo based digital phantoms indicated that selection of detector area is a critical system parameter in minimizing the error in concentration calculations. The findings here can guide the design of future fNIRS sensors.

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.

Classification of somatosensory cortex activities using fNIRS

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

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

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

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

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

The roles of miRNAs as potential biomarkers in lung diseases

MicroRNAs (miRNAs) are small non-coding RNAs which can act as master regulators of gene expression, modulate almost all biological process and are essential for maintaining cellular homeostasis. Dysregulation of miRNA expression has been associated with aberrant gene expression and may lead to pathological conditions. Evidence suggests that miRNA expression profiles are altered between health and disease and as such may be considered as biomarkers of disease. Evidence is increasing that miRNAs are particularly important in lung homeostasis and development and have been demonstrated to be the involved in many pulmonary diseases such as asthma, COPD, sarcoidosis, lung cancer and other smoking related diseases. Better understanding of the function of miRNA and the mechanisms underlying their action in the lung, would help to improve current diagnosis and therapeutics strategies in pulmonary diseases. Recently, some miRNA-based drugs have been introduced as possible therapeutic agents. In this review we aim to summarize the recent findings regarding the role of miRNAs in the airways and lung and emphasise their potential therapeutic roles in pulmonary diseases.

Bundled-Optode Method in Functional Near-Infrared Spectroscopy

In this paper, a theory for detection of the absolute concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) from hemodynamic responses using a bundled-optode configuration in functional near-infrared spectroscopy (fNIRS) is proposed. The proposed method is then applied to the identification of two fingers (i.e., little and thumb) during their flexion and extension. This experiment involves a continuous-wave-type dual-wavelength (760 and 830 nm) fNIRS and five healthy male subjects. The active brain locations of two finger movements are identified based on the analysis of the t- and p-values of the averaged HbOs, which are quite distinctive. Our experimental results, furthermore, revealed that the hemodynamic responses of two-finger movements are different: The mean, peak, and time-to-peak of little finger movements are higher than those of thumb movements. It is noteworthy that the developed method can be extended to 3-dimensional fNIRS imaging.

Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy

The paper presents a functional near-infrared spectroscopy (fNIRS)-based bundled-optode method for detection of the changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. fNIRS with 32 optodes is utilized to measure five healthy male subjects' brain-hemodynamic responses to arithmetic tasks. Specifically, the coordinates of 256 voxels in the three-dimensional (3D) volume are computed according to the known probe geometry. The mean path length factor in the Beer-Lambert equation is estimated as a function of the emitter-detector distance, which is utilized for computation of the absorption coefficient. The mean values of HbO and HbR obtained from the absorption coefficient are then applied for construction of a 3D fNIRS image. Our results show that the proposed method, as compared with the conventional approach, can detect brain activity with higher spatial resolution. This method can be extended for 3D fNIRS imaging in real-time applications.

Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application

In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI.

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.

Passive BCI based on drowsiness detection: an fNIRS study

We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left- and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.

Single-trial lie detection using a combined fNIRS-polygraph system

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph.

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.

Lateralization of music processing with noises in the auditory cortex: an fNIRS study

The present study is to determine the effects of background noise on the hemispheric lateralization in music processing by exposing 14 subjects to four different auditory environments: music segments only, noise segments only, music + noise segments, and the entire music interfered by noise segments. The hemodynamic responses in both hemispheres caused by the perception of music in 10 different conditions were measured using functional near-infrared spectroscopy. As a feature to distinguish stimulus-evoked hemodynamics, the difference between the mean and the minimum value of the hemodynamic response for a given stimulus was used. The right-hemispheric lateralization in music processing was about 75% (instead of continuous music, only music segments were heard). If the stimuli were only noises, the lateralization was about 65%. But, if the music was mixed with noises, the right-hemispheric lateralization has increased. Particularly, if the noise was a little bit lower than the music (i.e., music level 10~15%, noise level 10%), the entire subjects showed the right-hemispheric lateralization: This is due to the subjects' effort to hear the music in the presence of noises. However, too much noise has reduced the subjects' discerning efforts.

State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices

THE PAPER PRESENTS STATE SPACE MODELS OF THE HEMODYNAMIC RESPONSE (HR) OF FNIRS TO AN IMPULSE STIMULUS IN THREE BRAIN REGIONS: motor cortex (MC), somatosensory cortex (SC), and visual cortex (VC). Nineteen healthy subjects were examined. For each cortex, three impulse HRs experimentally obtained were averaged. The averaged signal was converted to a state space equation by using the subspace method. The activation peak and the undershoot peak of the oxy-hemoglobin (HbO) in MC are noticeably higher than those in SC and VC. The time-to-peaks of the HbO in three brain regions are almost the same (about 6.76 76 ± 0.2 s). The time to undershoot peak in VC is the largest among three. The HbO decreases in the early stage (~0.46 s) in MC and VC, but it is not so in SC. These findings were well described with the developed state space equations. Another advantage of the proposed method is its easy applicability in generating the expected HR to arbitrary stimuli in an online (or real-time) imaging. Experimental results are demonstrated.

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.