Dual-reporter fluorescent probe for precise identification of liver cancer by sequentially responding to carboxylesterase and polarity.

Early treatment significantly improves the survival rate of liver cancer patients, so the development of early diagnostic methods for liver cancer is urgent. Liver cancer can develop from viral hepatitis, alcoholic liver, and fatty liver, thus making the above diseases share common features such as elevated viscosity, reactive oxygen species, and reactive nitrogen species. Therefore, accurate differentiation between other liver diseases and liver cancer is both a paramount practical need and challenging. Numerous fluorescent probes have been reported for the diagnosis of liver cancer by detecting a single biomarker, but these probes lack specificity for liver cancer in complex biological systems. Obviously, using multiple liver cancer biomarkers as the basis for judgment can dramatically improve diagnostic accuracy. Herein, we report the first fluorescent probe, LD-TCE, that sequentially detects carboxylesterase (CE) and lipid droplet polarity in liver cancer cells with high sensitivity and selectivity, with linear detection of CE in the range of 0-6 U/mL and a 65-fold fluorescence enhancement in response to polarity. The probe first reacts with CE and releases weak fluorescence, which is then dramatically enhanced due to the decrease in lipid droplet polarity in liver cancer cells. This approach allows the probe to enable specific imaging of liver cancer with higher contrast and accuracy. The probe successfully achieved the screening of liver cancer cells and the precise identification of liver cancer in mice. More importantly, it is not disturbed by liver fibrosis, which is a common pathological feature of many liver diseases. We believe that the LD-TCE is expected to be a powerful tool for early diagnosis of liver cancer.

A long-wavelength mitochondria-targeted CO fluorescent probe for living cells and zebrafish imaging.

Carbon monoxide (CO) not only causes damage to life and health as an environmental pollutant, but also undertakes many physiological functions in organisms. In particular, developing means that can be used for the determination of CO in organelles will provide insight into the vital role it plays. Studies have shown that mitochondrial respiration is closely related to CO concentrations, so it is critical to develop tools for CO detection in mitochondria. Here, we use a rhodamine derivative that can target mitochondria as fluorophores to construct a mitochondrial-labeled CO fluorescence probe (Rh-CO) with high sensitivity (detection limit: 9.4 nM), excellent water-solubility, and long emission (λem = 630 nm). Prominently, the probe has outstanding mitochondria-targeting capabilities. Moreover, we used transient glucose deprivation (TGD) and heme to stimulate endogenous CO production in living cells and zebrafish, respectively, and the probe exhibited excellent imaging capabilities. All in all, we expect this probe to contribute to a deeper understanding of the role played by CO in mitochondria.

Influence of Transcutaneous Vagus Nerve Stimulation on Motor Planning: A Resting-State and Task-State EEG Study.

Transcutaneous vagus nerve stimulation (tVNS) shows a potential regulatory role for motor planning. Still, existing research mainly focuses on behavioral studies, and the neural modulation mechanism needs to be clarified. Therefore, we designed a multi-condition (active or sham, pre or under, difficult or easy, left-hand or right-hand) motor planning experiment to explore the effect of online tVNS (i.e., tVNS and tasks synchronized). Twenty-eight subjects were recruited and randomly assigned to active and sham groups. Both groups performed the same tasks in the experiment and separately collected task-state EEG and 5-min eye-open resting-state EEG. The results showed that the changes in event-related potential (ERP) and movement-related cortical potential (MRCP) amplitudes were more significant for the left-hand difficult task (LD) under active-tVNS. According to the power spectrum results, active-tVNS significantly modulated the activities of the contralateral motor cortex at beta and gamma bands in the resting state. The functional connectivity based on partial directed coherence (PDC) showed significant changes in the parietal lobe after active-tVNS. These findings suggest that tVNS is a promising way to improve motor planning ability.

Enhancing Motor Sequence Learning via Transcutaneous Auricular Vagus Nerve Stimulation (taVNS): An EEG Study.

Motor learning plays a crucial role in human life, and various neuromodulation methods have been utilized to strengthen or improve it. Transcutaneous auricular vagus nerve stimulation (taVNS) has gained increasing attention due to its non-invasive nature, affordability and ease of implementation. Although the potential of taVNS on regulating motor learning has been suggested, its actual regulatory effect has yet been fully explored. Electroencephalogram (EEG) analysis provides an in-depth understanding of cognitive processes involved in motor learning so as to offer methodological support for regulation of motor learning. To investigate the effect of taVNS on motor learning, this study recruited 22 healthy subjects to participate a single-blind, sham-controlled, and within-subject serial reaction time task (SRTT) experiment. Every subject involved in two sessions at least one week apart and received a 20-minute active/sham taVNS in each session. Behavioral indicators as well as EEG characteristics during the task state, were extracted and analyzed. The results revealed that compared to the sham group, the active group showed higher learning performance. Additionally, the EEG results indicated that after taVNS, the motor-related cortical potential amplitudes and alpha-gamma modulation index decreased significantly and functional connectivity based on partial directed coherence towards frontal lobe was enhanced. These findings suggest that taVNS can improve motor learning, mainly through enhancing cognitive and memory functions rather than simple movement learning. This study confirms the positive regulatory effect of taVNS on motor learning, which is particularly promising as it offers a potential avenue for enhancing motor skills and facilitating rehabilitation.

A Novel Complex-Valued Gaussian Measurement Matrix for Image Compressed Sensing.

The measurement matrix used influences the performance of image reconstruction in compressed sensing. To enhance the performance of image reconstruction in compressed sensing, two different Gaussian random matrices were orthogonalized via Gram-Schmidt orthogonalization, respectively. Then, one was used as the real part and the other as the imaginary part to construct a complex-valued Gaussian matrix. Furthermore, we sparsified the proposed measurement matrix to reduce the storage space and computation. The experimental results show that the complex-valued Gaussian matrix after orthogonalization has better image reconstruction performance, and the peak signal-to-noise ratio and structural similarity under different compression ratios are better than the real-valued measurement matrix. Moreover, the sparse measurement matrix can effectively reduce the amount of calculation.

Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities.

Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.

Enhancing Motor Imagery Performance by Antiphasic 10 Hz Transcranial Alternating Current Stimulation.

Motor imagery (MI), as a cognitive motor process, involves the coordinated activation of frontal and parietal cortices and has been widely studied as an effective way to improve motor functions. However, there are large inter-individual differences in MI performance, with many subjects unable to elicit sufficiently reliable MI brain patterns. It has been shown that dual-site transcranial alternating current stimulation (tACS) applied on two brain sites can modulate functional connectivity between the targeted regions. Here, we investigated whether electrically stimulating frontal and parietal regions using dual-site tACS at mu frequency will modulate motor imagery performance. Thirty-six healthy participants were recruited and randomly divided into in-phase (0° lag), anti-phase (180° lag) and sham stimulation group. All groups performed the simple (grasping movement) and complex (writing movement) motor imagery tasks before and after tACS. Simultaneously collected EEG data showed that the event-related desynchronization (ERD) of mu rhythm and classification accuracy during complex task were significantly improved after anti-phase stimulation. In addition, anti-phase stimulation resulted in decreased event-related functional connectivity between regions within frontoparietal network in the complex task. In contrast, no beneficial after-effects of anti-phase stimulation were found in the simple task. These findings suggest that dual-site tACS effects on MI dependent on the phase lag of the stimulation and the complexity of the task. Anti-phase stimulation applied to the frontoparietal regions is a promising way to foster demanding MI task.

Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training.

As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information (  âˆ¼  10% and  âˆ¼  18% improvement respectively), reaching  âˆ¼  89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation.

Motor Imagery and Action Observation Induced Electroencephalographic Activations to Guide Subject-Specific Training Paradigm: A Pilot Study.

Brain-computer interface (BCI)-based motor rehabilitation feedback training system can facilitate motor function reconstruction, but its rehabilitation mechanism with suitable training protocol is unclear, which affects the application effect. To this end, we probed the electroencephalographic (EEG) activations induced by motor imagery (MI) and action observation (AO) to provide an effective method to optimize motor feedback training. We grouped subjects according to their alpha-band sensorimotor cortical excitability under MI and AO conditions, and investigated the EEG response under the same paradigm between groups and different motor paradigms within group, respectively. The results showed that there were significant differences in sensorimotor activations between two groups of subjects. Specifically, the group with weaker MI induced EEG features, could achieve stronger sensorimotor activations in AO than that of other conditions. The group with stronger MI induced EEG features, could achieve stronger sensorimotor activations in the MI+AO than that of other conditions. We also explored their classification and brain network differences, which might try to explain the EEG mechanism in different individuals and help stroke patients to choose appropriate subject-specific motor training paradigm for their rehabilitation and better treatment outcomes.

An Image-Based Quantized Compressive Sensing Scheme Using Zadoff-Chu Measurement Matrix.

In this paper, a complex-valued Zadoff-Chu measurement matrix is proposed and used in an image-based quantized compressive sensing (CS) scheme. The results of theoretical analysis and simulations show that the reconstruction performance generated by the proposed Zadoff-Chu measurement matrix is better than that obtained by commonly used real-valued measurement matrices. We also applied block compressive sensing (BCS) to reduce the computational complexity of CS and analyzed the effect of block size on the reconstruction performance of the method. The results of simulations revealed that an appropriate choice of block size can not only reduce the computational complexity but also improve the accuracy of reconstruction. Moreover, we studied the effect of quantization on the reconstruction performance of image-based BCS through simulations, and the results showed that analog-to-digital converters with medium resolutions are sufficient to implement quantization and achieve comparable reconstruction performance to that obtained at high resolutions, based on which an image-based BCS framework with low power consumption can thus be developed.

Enhancing Visual-Guided Motor Imagery Performance via Sensory Threshold Somatosensory Electrical Stimulation Training.

OBJECTIVE: Motor imagery (MI) based brain- computer interface (BCI) has been widely studied as an effective way to enhance motor learning and promote motor recovery. However, the accuracy of MI-BCI heavily depends on whether subjects can perform MI tasks correctly, which largely limits the general application of MI-BCI. To overcome this limitation, a training strategy based on the combination of MI and sensory threshold somatosensory electrical stimulation (MI+st-SES) is proposed in this study. METHODS: Thirty healthy subjects were recruited and randomly divided into SES group and control group. Both groups performed left-hand and right-hand MI tasks in three consecutive blocks. The main difference between two groups lies in the second block, where subjects in SES group received the st-SES during MI tasks whereas the control group performed MI tasks only. RESULTS: The results showed that the SES group had a significant improvement in event-related desynchronization (ERD) of alpha rhythm after the training session of MI+st-SES (left-hand: F(2,27) = 9.98, p<0.01; right-hand: F(2, 27) = 10.43, p<0.01). The classification accuracy between left- and right-hand MI in the SES group was also significantly improved following MI+st-SES training (F(2,27) = 6.46, p<0.01). In contrary, there was no significant difference between the first and third blocks in the control group (F(2,27) = 0.18, p = 0.84). The functional connectivity based on weighted pairwise phase consistency (wPPC) over the sensorimotor area also showed an increase after the MI+st-SES training. CONCLUSION AND SIGNIFICANCE: Our findings indicate that training based on MI+st-SES is a promising way to foster MI performance and assist subjects in achieving efficient BCI control.

Resting-state electroencephalogram microstate to evaluate post-stroke rehabilitation and associate with clinical scales.

Introduction: Stroke is usually accompanied by a range of complications, like post-stroke motor disorders. So far, its evaluation of motor function is developed on clinical scales, such as Fugl-Meyer Assessment (FMA), Instrumental Activities of Daily Living (IADL), etc. These scale results from behavior and kinematic assessment are inevitably influenced by subjective factors, like the experience of patients and doctors, lacking neurological correlations and evidence. Methods: This paper applied a microstate model based on modified k-means clustering to analyze 64-channel electroencephalogram (EEG) from 12 stroke patients and 12 healthy volunteers, respectively, to explore the feasibility of applying microstate analysis to stroke patients. We aimed at finding some possible differences between stroke and healthy individuals in resting-state EEG microstate features. We further explored the correlations between EEG microstate features and scales within the stroke group. Results and discussion: By statistical analysis, we obtained significant differences in EEG microstate features between the stroke and healthy groups and significant correlations between microstate features and scales within the stroke group. These results might provide some neurological evidence and correlations in the perspective of EEG microstate analysis for post-stroke rehabilitation and evaluation of motor disorders. Our work suggests that microstate analysis of resting-state EEG is a promising method to assist clinical and assessment applications.

UiO-66-NH2 nanocomposites incorporated cellulose acetate for forward osmosis membranes of high desalination performance.

In this paper, the hydrophilic UiO-66-NH2 nanomaterial was synthesized by the solvent-thermal method and characterized. Then, UiO-66-NH2 was introduced into the casting membrane solution of cellulose acetate (CA) forward osmosis (FO) membrane, and CA/UiO-66-NH2 forward osmosis membrane was prepared by the phase inversion method. The optimum preparation conditions of CA/UiO-66-NH2 mixed matrix membranes were determined as follows: the content of UiO-66-NH2 was 0.4 wt%, the coagulation bath temperature was 35°C, the mixing temperature was 50°C and the heat treatment temperature was 50°C. FTIR, SEM, water contact angle and AFM were carried out on CA/UiO-66-NH2 forward osmosis membrane prepared under the best preparation conditions. Compared to the CA forward osmosis membrane, the permeability and selectivity of the CA/UiO-66-NH2 membrane were improved. The water flux and reverse salt flux of the CA/UiO-66-NH2 forward osmosis membrane reached 52.32 L/(m2·h) and 2.43 g/(m2·h), respectively. The permeability selectivity of CA membranes and CA/UiO-66-NH2 membranes did not change much during 180 min, indicating that the two membranes had good long-term stability. This study shows a potential advantage of UiO-66-NH2 as additives for improvement in the desalination performance of forward osmosis membranes.

Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke.

Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for patients with stroke in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. Motor imagery-based brain computer interface (MI-BCI) combined with functional electrical stimulation (FES) is a potential active neural rehabilitation technology for patients with stroke recently, which complements traditional passive rehabilitation methods. As the predecessor of BCI technology, neurofeedback training (NFT) is a psychological process that feeds back neural activities online to users for self-regulation. In this work, BCI-based NFT were proposed to promote the active repair and reconstruction of the whole nerve conduction pathway and motor function. We designed and implemented a multimodal, training type motor NFT system (BCI-NFT-FES) by integrating the visual, auditory, and tactile multisensory pathway feedback mode and using the joint detection of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The results indicated that after 4 weeks of training, the clinical scale score, event-related desynchronization (ERD) of EEG patterns, and cerebral oxygen response of patients with stroke were enhanced obviously. This study preliminarily verified the clinical effectiveness of the long-term NFT system and the prospect of motor function rehabilitation.

The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance.

Motor imagery-based brain-computer interface (MI-BCI) has been largely studied to improve motor learning and promote motor recovery. However, the difficulty in performing MI limits the widespread application of MI-BCI. It has been suggested that the usage of sensory threshold somatosensory electrical stimulation (st-SES) is a promising way to guide participants on MI tasks, but it is still unclear whether st-SES is effective for all users. In the present study, we aimed to examine the effects of st-SES on the MI-BCI performance in two BCI groups (High Performers and Low Performers). Twenty healthy participants were recruited to perform MI and resting tasks with EEG recordings. These tasks were modulated with or without st-SES. We demonstrated that st-SES improved the performance of MI-BCI in the Low Performers, but led to a decrease in the accuracy of MI-BCI in the High Performers. Furthermore, for the Low Performers, the combination of st-SES and MI resulted in significantly greater event-related desynchronization (ERD) and sample entropy of sensorimotor rhythm than MI alone. However, the ERD and sample entropy values of MI did not change significantly during the st-SES intervention in the High Performers. Moreover, we found that st-SES had an effect on the functional connectivity of the fronto-parietal network in the alpha band of Low Performers and the beta band of High Performers, respectively. Our results demonstrated that somatosensory input based on st-SES was only beneficial for sensorimotor cortical activation and MI-BCI performance in the Low Performers, but not in the High Performers. These findings help to optimize guidance strategies to adapt to different categories of users in the practical application of MI-BCI.

The preparation of ultrastable Al3+ doped CeO2 supported Au catalysts: Strong metal-support interaction for superior catalytic activity towards CO oxidation.

The classical strong metal-support interaction (SMSI) plays a key role in improving thermal stability for supported Au catalysts. However, it always decreases the catalytic activity because of the encapsulation of Au species by support. Herein, we demonstrate that Al3+ is a functional additive which could effectively improve both catalytic activity and sintering resistant property for H2 pretreated Al3+ doped CeO2 supported Au (AuCeAl) catalyst at high temperature. The physical characterization and in-situ DRIFTS results provide insight that more oxygen vacancies generated by Al3+ doping could be as preferential adsorption sites for CO molecules when the encapsulation of Au species occurred, which is certificated by an accelerated formation of bicarbonate species. In the meantime, smaller Au nanoparticles with higher dispersion (2.8 nm, 85.63%) is achieved in AuCeAl catalysts, compared with that in CeO2 supported Au (AuCe) catalysts (5.1 nm, 36.17%). Additionally, the as-prepared AuCeAl catalysts also have superior catalytic performance even after calcination at 800 °C in air.

Broadband Circular Polarizer Based on Chirped Double-Helix Chiral Fiber Grating.

We propose an all-fiber broadband circular polarizer based on leaky mode coupling and a phase-matched turning point (PMTP) in a chirped, double-helix, chiral, long-period, fiber grating (CLPG). The CLPG was coated with a material in which the refractive index was higher than that of the fiber cladding, enabling the coupling of the core mode to leaky modes to achieve a desired extinction ratio. The complex coupled-mode theory was employed to investigate the coupling mechanism and conditions under which the desired coupling efficiency could be achieved. Moreover, the PMTP in phase-matched curves, which resolved the conflict between the operating bandwidth and the grating pitch range of the CLPG and made a large bandwidth with a small grating pitch possible, was used in the design to achieve a compact structure. Finally, two broadband circular polarizers with an extinction ratio above 25 dB were simulated; one had a bandwidth of over 120 nm and a length of 3.5 cm, and the other had a bandwidth of over 300 nm and a length of 8 cm.

Desalination Characteristics of Cellulose Acetate FO Membrane Incorporated with ZIF-8 Nanoparticles.

Forward osmosis membranes have a wide range of applications in the field of water treatment. However, the application of seawater desalination is restricted, so the research of forward osmosis membranes for seawater desalination poses new challenges. In this study, zeolitic imidazolate framework-8 (ZIF-8) was synthesized by a mechanical stirring method, and its crystal structure, surface morphology, functional group characteristics, thermochemical stability, pore size distribution and specific surface area were analyzed. The cellulose acetate (CA)/ZIF-8 mixed matrix forward osmosis membrane was prepared by using the synthesized ZIF-8 as a modified additive. The effects of the additive ZIF-8 content, coagulation bath temperature, mixing temperature and heat treatment temperature on the properties of the CA/ZIF-8 forward osmosis membrane were systematically studied, and the causes were analyzed to determine the best membrane preparation parameters. The structure of the CA membrane and CA/ZIF-8 mixed matrix forward osmosis membranes prepared under the optimal conditions were characterized by Fourier Transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), contact angle and Atomic force microscope (AFM). Finally, the properties of the HTI membrane (Membrane manufactured by Hydration Technology Innovations Inc.), CA forward osmosis membrane and CA/ZIF-8 mixed matrix forward osmosis membrane were compared under laboratory conditions. For the CA membrane, the water flux and reverse salt flux reached 48.85 L·m-2·h-1 and 3.4 g·m-2·h-1, respectively. The reverse salt flux and water flux of the CA/ZIF-8 membrane are 2.84 g·m-2·h-1 and 50.14 L·m-2·h-1, respectively. ZIF-8 has a promising application in seawater desalination.

Effects of Transcutaneous Vagus Nerve Stimulation (tVNS) on Action Planning: A Behavioural and EEG Study.

Action planning is an important decision-making process, which can be specially affected by environment. Response selection during action planning has been demonstrated to be modulated by tVNS. Therefore, tVNS shows a great potential for modulating the action planning process. We aimed to explore the tVNS-induced effect on action planning in behavioural and electrophysiology. Twenty-eight participants were randomly divided into two groups (active group and sham group). A single-blind, sham-controlled between-subject design was applied to explore the effect of online-tVNS (i.e., tVNS overlapping with the task) on action planning paradigm. We measured and compared reaction time (RT) and movement-related cortical potentials (MRCPs) before and after tVNS between active and sham groups. As compared to sham group, for the ipsilateral hand/contralateral hemisphere relative to the stimulated side, active tVNS significantly reduced the reaction time and decreased the MRCP amplitude mainly in the challenging tasks. Our results indicate that tVNS can produce a lateralization effect on action planning, especially plays an important role in the more challenging tasks as reflected both in the behavioural and electrophysiological results.

A novel approach to automatic sleep stage classification using forehead electrophysiological signals.

Background: Sleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment. Method: In this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems. We extract 3 channel signals from the forehead, named forehead electroencephalogram 1 (Fh1), forehead electroencephalogram 2 (Fh2), and forehead center electrooculogram (Fhz). Spectral features, statistical features, and entropy features are extracted using the discrete wavelet transform (DWT) method. Light gradient boosting machine (LGB), random forest (RF), and support vector machine (SVM) are employed to classify four stages: awake, light sleep (LS), deep sleep (DS), and rapid eye movement (REM). Result: The performance of the proposed method is validated using databases of the Sleep-EDFX and our Own-data, which include polysomnograms (PSGs) and forehead signals of 28 subjects. The overall classification accuracy of using the combination of Fh1, Fh2, and Fhz can reach up to 90.25% accuracy with a kappa coefficient of 0.857. Conclusions: The proposed method could provide state-of-art multichannel sleep stage scoring performance with higher portability. This will facilitate the application of long-term monitoring of sleep quality in the future.