Toward the Production of Super Graphene.

The quality requirements of graphene depend on the applications. Some have a high tolerance for graphene quality and even require some defects, while others require graphene as perfect as possible to achieve good performance. So far, synthesis of large-area graphene films by chemical vapor deposition of carbon precursors on metal substrates, especially on Cu, remains the main way to produce high-quality graphene, which has been significantly developed in the past 15 years. However, although many prototypes are demonstrated, their performance is still more or less far from the theoretical property limit of graphene. This review focuses on how to make super graphene, namely graphene with a perfect structure and free of contaminations. More specially, this study focuses on graphene synthesis on Cu substrates. Typical defects in graphene are first discussed together with the formation mechanisms and how they are characterized normally, followed with a brief review of graphene properties and the effects of defects. Then, the synthesis progress of super graphene from the aspects of substrate, grain size, wrinkles, contamination, adlayers, and point defects are reviewed. Graphene transfer is briefly discussed as well. Finally, the challenges to make super graphene are discussed and a strategy is proposed.

Brain frailty associated with stroke events in anterior circulation large artery occlusion.

OBJECTIVE: To investigate the factors associated with brain frailty and the effect of brain frailty in patients with anterior circulation large artery occlusion (AC-LAO). METHODS: 1100 patients with AC-LVO consecutively admitted to the Second Hospital of Hebei Medical University, North China between June 2016 and April 2018 were retrospectively analyzed. The variables associated with brain frailty and stroke outcome were analyzed by ANOVA analysis, the Mann-Whitney U test and multiple linear regression. Based on previous research. Brain frailty score comprises 1 point each for white matter hyperintensity (WMH), old infarction lesions, and cerebral atrophy among 983 participants with baseline brain magnetic resonance imaging or computed tomography. RESULTS: Among AC-LAO participants, baseline brain frailty score ≥ 1 was common (750/983, 76.3%). Duration of hypertension > 5 years (mean difference [MD] 0.236, 95% CI 0.077, 0.395, p = 0.004), multiple vessel occlusion (MD 0.339, 95% CI 0.068, 0.611, p = 0.014) and basal ganglia infarction (MD -0.308, 95% CI -0.456, -0.160, p < 0.001) were independently associated with brain frailty score. Brain frailty score was independently associated with stroke events, and higher brain frailty scores were associated with higher rates of stroke events (p < 0.001). However, brain frailty has no independent effect on short-term outcome of ACI in AC-LAO patients. CONCLUSIONS: In AC-LAO patients, older age, duration of hypertension > 5 years, and multiple vessel occlusion influenced the brain frailty score. Brain frailty score was independently associated with the occurrence of stroke events in AC-LAO patients.

Recurrent Cerebral Infarction Due to Moyamoya Disease Complicated With Systemic Lupus Erythematosus: A Case Report and Literature Review.

INTRODUCTION: We report a rare case of moyamoya disease caused by an RNF213 mutation, complicated with systemic lupus erythematosus. CASE REPORT: A 32-year-old woman experienced 4 cerebral ischemia stroke events within 6 months. The main symptom was left limb weakness with blurred vision in the right eye. Results of digital subtraction angiography conducted at another hospital were consistent with moyamoya disease. On genetic testing, we found that the patient carried 2 mutations in the moyamoya disease-related gene RNF213 (p.R4810K, p.T1727M). On the basis of the laboratory immunologic indicators, such as positive antibodies and abnormal immunoglobulin levels and imaging examinations, the patient was finally diagnosed as moyamoya disease complicated with systemic lupus erythematosus. She was treated with aspirin, butylphthalide, urinary kallidinogenase, and sodium methylprednisolone. CONCLUSIONS: This was a 32-year-old young patient diagnosed with moyamoya disease carrying RNF213 gene mutation and accompanied by lupus with cerebral ischemic event as the first occurrence. The patient's condition was complex; therefore, comprehensive analysis and in-depth consideration were needed to avoid a missed diagnosis and misdiagnosis. When the primary disease cannot be identified, genetic testing can help to clarify the diagnosis of moyamoya disease.

Site-specific periosteal cells with distinct osteogenic and angiogenic characteristics.

OBJECTIVES: This study aimed to investigate the site-specific characteristics of rat mandible periosteal cells (MPCs) and tibia periosteal cells (TPCs) to assess the potential application of periosteal cells (PCs) in bone tissue engineering (BTE). MATERIALS AND METHODS: MPCs and TPCs were isolated and characterized. The potential of proliferation, migration, osteogenesis and adipogenesis of MPCs and TPCs were evaluated by CCK-8, scratch assay, Transwell assay, alkaline phosphatase staining and activity, Alizarin Red S staining, RT‒qPCR, and Western blot (WB) assays, respectively. Then, these cells were cocultured with human umbilical vein endothelial cells (HUVECs) to investigate their angiogenic capacity, which was assessed by scratch assay, Transwell assay, Matrigel tube formation assay, RT‒qPCR, and WB assays. RESULTS: MPCs exhibited higher osteogenic potential, higher alkaline phosphatase activity, and more mineralized nodule formation, while TPCs showed a greater capability for proliferation, migration, and adipogenesis. MPCs showed higher expression of angiogenic factors, and the conditioned medium of MPCs accelerated the migration of HUVECs, while MPC- conditioned medium induced the formation of more tubular structure in HUVECs in vitro. These data suggest that compared to TPCs, MPCs exert more consequential proangiogenic effects on HUVECs. CONCLUSIONS: PCs possess skeletal site-specific differences in biological characteristics. MPCs exhibit more eminent osteogenic and angiogenic potentials, which highlights the potential application of MPCs for BTE. CLINICAL RELEVANCE: Autologous bone grafting as the main modality for maxillofacial bone defect repair has many limitations. Constituting an important cell type in bone repair and regeneration, MPCs show greater potential for application in BTE, which provides a promising treatment option for maxillofacial bone defect repair.

FBLPF-ABOW: An Effective Method for Blink Artifact Removal in Single-Channel EEG Signal.

OBJECTIVE: The latest development in low-cost single-channel Electroencephalography (EEG) devices is gaining widespread attention because it reduces hardware complexity. Discrete wavelet transform (DWT) has been a popular solution to eliminate the blink artifacts in EEG signals. However, the existing DWT-based methods share the same wavelet function among subjects, which ignores the individual difference. To remedy this deficiency, this article proposes a novel approach to eliminate the blink artifacts in single-channel EEG signals. METHODS: Firstly, the forward-backward low-pass filter (FBLPF) and a fixed-length window are used to detect blink artifact intervals. Secondly, the adaptive bi-orthogonal wavelet (ABOW) is constructed based on the most representative blink signal. Thirdly, these detected signals are filtered by ABOW-DWT. The DWT's decomposition depth is automatically chosen by a similarity-based method. RESULTS: Compared to eight state-of-the-art methods, experiments on semi-simulated and real EEG signals demonstrate the proposed method's superiority in removing the blink artifacts with less neural information loss. SIGNIFICANCE: To filter the blink artifacts in single-channel EEG signals, the innovative idea of constructing an adaptive wavelet function based on the signal characteristics rather than using the conventional wavelet is proposed for the first time.

A deep domain adaptation framework with correlation alignment for EEG-based motor imagery classification.

It is impractical to collect sufficient and well-labeled EEG data in Brain-computer interface because of the time-consuming data acquisition and costly annotation. Conventional classification methods reusing EEG data from different subjects and time periods (across domains) significantly decrease the classification accuracy of motor imagery. In this paper, we propose a deep domain adaptation framework with correlation alignment (DDAF-CORAL) to solve the problem of distribution divergence for motor imagery classification across domains. Specifically, a two-stage framework is adopted to extract deep features for raw EEG data. The distribution divergence caused by subjected-related and time-related variations is further minimized by aligning the covariance of the source and target EEG feature distributions. Finally, the classification loss and adaptation loss are optimized simultaneously to achieve sufficient discriminative classification performance and low feature distribution divergence. Extensive experiments on three EEG datasets demonstrate that our proposed method can effectively reduce the distribution divergence between the source and target EEG data. The results show that our proposed method delivers outperformance (an average classification accuracy of 92.9% for within-session, an average kappa value of 0.761 for cross-session, and an average classification accuracy of 83.3% for cross-subject) in two-class classification tasks compared to other state-of-the-art methods.

Cerebral collateral circulation as an independent predictor for in-stent restenosis after carotid artery stenting.

Background: In-stent restenosis is a crucial problem after carotid artery stenting, but the exact predictors of in-stent restenosis remain unclear. We aimed to evaluate the effect of cerebral collateral circulation on in-stent restenosis after carotid artery stenting and to establish a clinical prediction model for in-stent restenosis. Methods: This retrospective case-control study enrolled 296 patients with severe carotid artery stenosis of C1 segment (≥70%) who underwent stent therapy from June 2015 to December 2018. Based on follow-up data, the patients were divided into the in-stent restenosis and no in-stent restenosis groups. The collateral circulation of the brain was graded according to the criteria of the American Society for Interventional and Therapy Neuroradiology/Society for Interventional Radiology (ASITN/SIR). Clinical data were collected, such as age, sex, traditional vascular risk factors, blood cell count, high-sensitivity C-reactive protein, uric acid, stenosis degree before stenting and residual stenosis rate, and medication after stenting. Binary logistic regression analysis was performed to identify potential predictors of in-stent restenosis, and a clinical prediction model for in-stent restenosis after carotid artery stenting was established. Results: Binary logistic regression analysis showed that poor collateral circulation was an independent predictor of in-stent restenosis (P=0.003). We also found that a 1% increase in residual stenosis rate was associated with a 9% increase in the risk of in-stent restenosis (P=0.02). Ischemic stroke history (P=0.03), family history of ischemic stroke (P<0.001), in-stent restenosis history (P<0.001), and nonstandard medication after stenting (P=0.04) were predictors of in-stent restenosis. The risk of in-stent restenosis was lowest when the residual stenosis rate was 12.5% after carotid artery stenting. Furthermore, we used some significant parameters to construct a binary logistic regression prediction model for in-stent restenosis after carotid artery stenting in the form of a nomogram. Conclusions: Collateral circulation is an independent predictor of in-stent restenosis after successful carotid artery stenting, and the residual stenosis rate tends to be below 12.5% to reduce restenosis risk. The standard medication should be strictly carried out for patients after stenting to prevent in-stent restenosis.

Construction and analysis of functional brain network based on emotional electroencephalogram.

Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG). Firstly, we defined electrode node as brain area of vicinity of electrode to construct 32-node small-scale FBN. Pearson correlation coefficient (PCC) was used to construct correlation-based FBNs. Phase locking value (PLV) and phase synchronization index (PSI) were utilized to construct synchrony-based FBNs. Next, global properties and effects of emotion of different networks were compared. The difference of synchrony-based FBN concentrates in alpha band, and the number of differences is less than that of correlation-based FBN. Node properties of different small-scale FBNs have significant differences, offering a new basis for feature extraction of recognition regions in emotional FBNs. Later, we made partition of electrode nodes and 10 new brain areas were defined as regional nodes to construct 10-node large-scale FBN. Results show the impact of emotion on network clusters on the right forehead, and high valence enhances information processing efficiency of FBN by promoting connections in brain areas.

Receptor-Interacting Protein 3/Calmodulin-Dependent Kinase II/Proline-Rich Tyrosine Kinase 2 Pathway is Involved in Programmed Cell Death in a Mouse Model of Brain Ischaemic Stroke.

Neuronal necroptosis and apoptosis are the most important pathways for programmed cell death after brain ischaemic stroke. Although apoptosis signalling pathways have been extensively studied, molecular mechanisms underlying necroptosis remain unclear. In this study, we found that receptor-interacting protein 3 (RIP3) deficiency reduced cerebral infarction volume, neurological deficits, and neuronal ultrastructural damage in a mouse model of brain ischaemic stroke by inhibiting programmed cell death. RIP3 deficiency inhibited the activation of both calmodulin-dependent kinase II (CaMKII) and proline-rich tyrosine kinase 2 (Pyk2) cascade, decreased the expression of classic necroptotic and apoptotic proteins, and ultimately decreased neuronal necroptosis and apoptosis. We further confirmed that RIP3 deficiency inhibited the decrease of mitochondrial membrane potential, the increase of calcium influx and reactive oxygen species (ROS) production. In addition, compared with WT primary cortical neurons, the decreased expression of CaMKII and Pyk2 was further verified in a Ripk3-/- primary cortical neurons underlying oxygen and glucose deprivation/reoxygenation (OGD/R) model. In conclusion, we first identified that the RIP3/CaMKII/Pyk2 pathway is involved in programmed cell death after brain ischaemic stroke, which suggests it is a promising therapeutic target in ischaemia-induced neuronal injury.

Distribution Adaptation and Classification Framework Based on Multiple Kernel Learning for Motor Imagery BCI Illiteracy.

A brain-computer interface (BCI) translates a user's thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of BCI illiterate subjects are low classification rates and poor repeatability. To address the problem of MI-BCI illiteracy, we propose a distribution adaptation method based on multi-kernel learning to make the distribution of features between the source domain and target domain become even closer to each other, while the divisibility of categories is maximized. Inspired by the kernel trick, we adopted a multiple-kernel-based extreme learning machine to train the labeled source-domain data to find a new high-dimensional subspace that maximizes data divisibility, and then use multiple-kernel-based maximum mean discrepancy to conduct distribution adaptation to eliminate the difference in feature distribution between domains in the new subspace. In light of the high dimension of features of MI-BCI illiteracy, random forest, which can effectively handle high-dimensional features without additional cross-validation, was employed as a classifier. The proposed method was validated on an open dataset. The experimental results show that that the method we proposed suits MI-BCI illiteracy and can reduce the inter-domain differences, resulting in a reduction in the performance degradation of both cross-subjects and cross-sessions.

Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion.

Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission. Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked. Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57-0.74) for LR, 0.66 (95% CI 0.57-0.74) for RLR, 0.55 (95% CI 0.45-0.64) for RF and 0.67 (95% CI 0.58-0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts. Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.

Frequency-Modulated Continuous Wave Radar Respiratory Pattern Detection Technology Based on Multifeature.

Respiratory diseases including apnea are often accompanied by abnormal respiratory depth, frequency, and rhythm. If different abnormal respiratory patterns can be detected and recorded, with their depth, frequency, and rhythm analyzed, the detection and diagnosis of respiratory diseases can be achieved. High-frequency millimeter-wave radar (76-81 GHz) has low environmental impact, high accuracy, and small volume, which is more suitable for respiratory signal detection and recognition compared with other contact equipment. In this paper, the experimental platform of frequency-modulated continuous wave (FMCW) radar was built at first, realizing the noncontact measurement of vital signs. Secondly, the energy intensity and threshold of respiration signal during each period were calculated by using the rectangular window, and the accurate judgment of apnea was realized via numerical comparison. Thirdly, the features of respiratory and heart rate signals, the number of peaks and valleys, the difference between peaks and valleys, the average and the standard deviation of normalized short-term energy, and the average and the standard deviation and the minimum of instantaneous frequency, were extracted and analyzed. Finally, support vector machine (SVM) and K-nearest neighbor (KNN) were used to classify the extracted features, and the accuracy was 98.25% and 88.75%, respectively. The classification and recognition of respiratory patterns have been successfully realized.

Subtypes of anterior circulation large artery occlusions with acute brain ischemic stroke.

Anterior circulation large artery occlusion (AC-LAO) related acute ischemic stroke (AIS) is particularly common in clinics in China. We retrospectively analyzed 787 consecutively hospitalized AIS patients with AC-LAO in Hebei Province, China. AC-LAO was defined as a complete occlusion of at least one intracranial internal carotid artery (ICA) or middle cerebral artery (MCA) based on computed tomography or magnetic resonance angiography. Among eight subtypes of AC-LAO, unilateral MCA occlusion is the most common one (49.8%, n = 392), while bilateral ICA/unilateral MCA occlusion is the least (0.3%, n = 2). Compared with unilateral MCA and unilateral ICA occlusion, patients with tandem ICA/MCA and bilateral ICA/MCA occlusion had poor outcomes after suffering AIS. Age (OR 1.022; 95%CI, 1.007 to 1.036) was an independent risk factor for single artery progressed to multiple artery occlusion, while ApoA1 (OR 0.453; 95% CI, 0.235 to 0.953) was a protective factor. Patients with unilateral MCA occlusion were prone to artery-to-artery embolism infarction subtype, unilateral ICA occlusion group were the most vulnerable to hypoperfusion/impaired emboli clearance subtype. Our results suggested various AC-LAO subtypes have different clinical characteristics and prognosis and were prone to different subtypes of infarction. Customized preventive measures based on AC-LAO subtypes may be more targeted preventions of stroke recurrences for AIS patients and could improve their prognoses.

Physiological interference reduction for near infrared spectroscopy brain activity measurement based on recursive least squares adaptive filtering and least squares support vector machines.

Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.

Influence of extracerebral layers on estimates of optical properties with continuous wave near infrared spectroscopy: analysis based on multi-layered brain tissue architecture and Monte Carlo simulation.

Continuous wave near-infrared spectroscopy (CW-NIRS) can be used to measure cerebral activity because it is noninvasive, simple and portable. However, the performance of the continuous wave near-infrared spectroscopy is distorted by the presence of extracerebral layer. Change of optical parameters in gray matter layer will then be inappropriately converted into the brain activity response. In the current study, a five-layer structure model constitute of scalp, skull, cerebrospinal fluid, gray matter and white matter, have been applied to fabricate human brain tissue. The phantom is made by the mixture of the Intralipid, India ink and agar. The optical parameters of gray matter layer can be flexibly adjusted to simulate the change of the deep brain tissue. The near infrared optical measurement system was designed to detect the changes in the absorption coefficients of the gray matter and quantitative analyze the influence of the extracerebral layers. Monte Carlo technique for the equivalent multi-layered brain tissue models is then performed to compensate partial volume effect introduced by the extracerebral layers. The results of the experiments suggested that the extracerebral layers influence the measurement and the influence of the extracerebral layers can be suppressed by correcting partial volume effect using Monte Carlo simulations.

A study on quality assessment of the surface EEG signal based on fuzzy comprehensive evaluation method.

Surface EEG (Electroencephalography) signal is vulnerable to interference due to its characteristics and sampling methods. So it is of great importance to evaluate the collected EEG signal prior to use. Traditional methods usually use the impedance between skin and electrode to estimate the quality of the EEG signal, which has shortcomings such as monotonous features, high false positive rates, and poor real-time capability. Aiming at addressing these issues, this paper presents a novel model of EEG quality assessment based on Fuzzy Comprehensive Evaluation method. The developed model employs amplitude, power frequency ratio, and alpha band PSD (Power Spectral Density) ratio of resting EEG signal as evaluation factors, and performs a quantitative assessment of the signal quality. Experiments show that the proposed model can significantly determine the EEG signal quality. In addition, the model is simple in implementation with low computational complexity, and is able to present the EEG quality evaluation results in real time. Before the formal measurement, collecting short-term resting EEG data, and evaluating the EEG signal quality and current signal acquisition environment using the model, the collection efficiency of qualified EEG signals can be greatly improved.

Multi-subject subspace alignment for non-stationary EEG-based emotion recognition.

Emotion recognition based on EEG signals is a critical component in Human-Machine collaborative environments and psychiatric health diagnoses. However, EEG patterns have been found to vary across subjects due to user fatigue, different electrode placements, and varying impedances, etc. This problem renders the performance of EEG-based emotion recognition highly specific to subjects, requiring time-consuming individual calibration sessions to adapt an emotion recognition system to new subjects. Recently, domain adaptation (DA) strategies have achieved a great deal success in dealing with inter-subject adaptation. However, most of them can only adapt one subject to another subject, which limits their applicability in real-world scenarios. To alleviate this issue, a novel unsupervised DA strategy called Multi-Subject Subspace Alignment (MSSA) is proposed in this paper, which takes advantage of subspace alignment solution and multi-subject information in a unified framework to build personalized models without user-specific labeled data. Experiments on a public EEG dataset known as SEED verify the effectiveness and superiority of MSSA over other state of the art methods for dealing with multi-subject scenarios.

Human emotion classification based on multiple physiological signals by wearable system.

BACKGROUND: Human emotion classification is traditionally achieved using multi-channel electroencephalogram (EEG) signal, which requires costly equipment and complex classification algorithms. OBJECTIVE: The experiments can be implemented in the laboratory environment equipped with high-performance computers for the online analysis; this will hinder the usability in practical applications. METHODS: Considering that other physiological signals are also associated with emotional changes, this paper proposes to use a wearable, wireless system to acquire a single-channel electroencephalogram signal, respiration, electrocardiogram (ECG) signal, and body postures to explore the relationship between these signals and the human emotions. RESULTS AND CONCLUSIONS: Compared with traditional emotion classification method, the presented method was able to extract a small number of key features associated with human emotions from multiple physiological signals, where the algorithm complexity was greatly reduced when incorporated with the support vector machine classification. The proposed method can support an embedded on-line analysis and may enhance the usability of emotion classification.

Evoked Hemodynamic Response Estimation to Auditory Stimulus Using Recursive Least Squares Adaptive Filtering with Multidistance Measurement of Near-Infrared Spectroscopy.

The performance of functional near-infrared spectroscopy (fNIRS) is sometimes degraded by the interference caused by the physical or the systemic physiological activities. Several interferences presented during fNIRS recordings are mainly induced by cardiac pulse, breathing, and spontaneous physiological low-frequency oscillations. In previous work, we introduced a multidistance measurement to reduce physiological interference based on recursive least squares (RLS) adaptive filtering. Monte Carlo simulations have been implemented to evaluate the performance of RLS adaptive filtering. However, its suitability and performance on human data still remain to be evaluated. Here, we address the issue of how to detect evoked hemodynamic response to auditory stimulus using RLS adaptive filtering method. A multidistance probe based on continuous wave fNIRS is devised to achieve the fNIRS measurement and further study the brain functional activation. This study verifies our previous findings that RLS adaptive filtering is an effective method to suppress global interference and also provides a practical way for real-time detecting brain activity based on multidistance measurement.

An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI.

EEG signals have weak intensity, low signal-to-noise ratio, non-stationary, non-linear, time-frequency-spatial characteristics. Therefore, it is important to extract adaptive and robust features that reflect time, frequency and spatial characteristics. This paper proposes an effective feature extraction method WDPSD (feature extraction from the Weighted Difference of Power Spectral Density in an optimal channel couple) that can reflect time, frequency and spatial characteristics for 2-class motor imagery-based BCI system. In the WDPSD method, firstly, Power Spectral Density (PSD) matrices of EEG signals are calculated in all channels, and an optimal channel couple is selected from all possible channel couples by checking non-stationary and class separability, and then a weight matrix which reflects non-stationary of PSD difference matrix in selected channel couple is calculated; finally, the robust and adaptive features are extracted from the PSD difference matrix weighted by the weight matrix. The proposed method is evaluated from EEG signals of BCI Competition IV Dataset 2a and Dataset 2b. The experimental results show a good classification accuracy in single session, session-to-session, and the different types of 2-class motor imagery for different subjects.