The effect of rhythmic stimuli with spatial information on sensorimotor synchronization: an EEG and EMG study.

Introduction: Sensorimotor synchronization (SMS) is the human ability to align body movement rhythms with external rhythmic stimuli. While the effects of rhythmic stimuli containing only temporal information on SMS have been extensively studied, less is known about how spatial information affects SMS performance. This study investigates the neural mechanisms underlying SMS with rhythmic stimuli that include both temporal and spatial information, providing insights into the influence of these factors across different sensory modalities. Methods: This study compared the effects temporal information and spatial information on SMS performance across different stimuli conditions. We simultaneously recorded the electroencephalogram (EEG), the electromyogram (EMG), and behavioral data as subjects performed synchronized tapping to rhythmic stimuli. The study analyzed SMS performance under conditions including auditory, visual, and auditory-visual motion stimuli (containing both temporal and spatial information), as well as auditory, visual, and auditory-visual non-motion stimuli (containing only temporal information). Specifically, the research examined behavioral data (i.e., mean asynchrony, absolute asynchrony, and variability), neural oscillations, cortico-muscular coherence (CMC), and brain connectivity. Results: The results demonstrated that SMS performance was superior with rhythmic stimuli containing both temporal and spatial information compared to stimuli with only temporal information. Moreover, sensory-motor neural entrainment was stronger during SMS with rhythmic stimuli containing spatial information within the same sensory modality. SMS with both types of rhythmic stimuli was found to be dynamically modulated by neural oscillations and cortical-muscular coupling in the beta band (13-30 Hz). Discussion: These findings provide deeper insights into the combined effects of temporal and spatial information, as well as sensory modality, on SMS performance. The study highlights the dynamic modulation of SMS by neural oscillations and CMC, particularly in the beta band, offering valuable contributions to understanding the neural basis of sensorimotor synchronization.

High-Performance Rotating Structure Triboelectric-Electromagnetic Hybrid Nanogenerator for Environmental Wind Energy Harvesting.

As environmental energy harvesting gains increasing importance in self-powered systems and large-scale energy demands, wind energy, as a clean, pollution-free, and renewable source, has garnered widespread attention. However, achieving efficient wind energy collection remains challenging. This study proposes a high-performance rotating structure triboelectric-electromagnetic hybrid nanogenerator designed for environmental wind energy harvesting. By optimizing the magnetic circuit design of the electromagnetic generator, the dispersed radial magnetic field is converted into a unified axial magnetic field, enabling efficient power generation with only a single annular coil, thereby simplifying the generator design and reducing manufacturing and maintenance costs. Additionally, a triboelectric nanogenerator design with soft contact friction between polycarbonate (PC) fur and fluorinated ethylene propylene (FEP) film was implemented, optimizing the spacing between the electrode and friction layers, thus enhancing output performance and device durability. Furthermore, we simulated and experimentally tested the output waveform of the designed hybrid generator structure, with the results showing a high degree of similarity, further validating the rationality of the device design and providing guidance for structural optimization. Subsequently, we achieved efficient energy storage using an energy management circuit (EMC). With the integration of the EMC, the generator successfully powered a Bluetooth temperature and humidity sensor at a wind speed of 10 m/s, achieving wireless transmission, and demonstrating its potential application in traffic signal systems and other natural environmental systems. This research provides an important reference for further exploration of novel wind energy harvesting technologies.

20.2% Efficiency Organic Photovoltaics Employing a π-Extension Quinoxaline-Based Acceptor with Ordered Arrangement.

Organic solar cells, as a cutting-edge sustainable renewable energy technology, possess a myriad of potential applications, while the bottleneck problem of less than 20% efficiency limits the further development. Simultaneously achieving an ordered molecular arrangement, appropriate crystalline domain size, and reduced nonradiative recombination poses a significant challenge and is pivotal for overcoming efficiency limitations. This study employs a dual strategy involving the development of a novel acceptor and ternary blending to address this challenge. A novel non-fullerene acceptor, SMA, characterized by a highly ordered arrangement and high lowest unoccupied molecular orbital energy level, is synthesized. By incorporating SMA as a guest acceptor in the PM6:BTP-eC9 system, it is observed that SMA staggered the liquid-solid transition of donor and acceptor, facilitating acceptor crystallization and ordering while maintaining a suitable domain size. Furthermore, SMA optimized the vertical morphology and reduced bimolecular recombination. As a result, the ternary device achieved a champion efficiency of 20.22%, accompanied by increased voltage, short-circuit current density, and fill factor. Notably, a stabilized efficiency of 18.42% is attained for flexible devices. This study underscores the significant potential of a synergistic approach integrating acceptor material innovation and ternary blending techniques for optimizing bulk heterojunction morphology and photovoltaic performance.

Flexible Sensor for Real-Time Monitoring of Motion Artifacts in Magnetic Resonance Imaging.

In recent years, magnetic resonance imaging has been widely used in the medical field. During the scan, if the human body moves, then there will be motion artifacts on the scan image, which will interfere with the diagnosis and only be found after the end of the scan sequence, resulting in a waste of manpower and resources. However, there is a lack of technology that halts scanning once motion artifacts arise. Here, we designed a real-time monitoring sensor (RMS) to dynamically perceive the movement of the human body and to pause in time when the movement exceeds a certain amplitude. The sensor has an array structure that can accurately sense the position of the human body in real time. The selection of the RMS ensures that there is no additional interference with the scanning results. Based on this design, the RMS can achieve the monitoring function of motion artifact generation.

Investigating the effects of rare ginsenosides on hyperuricemia and associated sperm damage via nontargeted metabolomics and gut microbiota.

ETHNOPHARMACOLOGICAL RELEVANCE: In ancient times, ginseng was used for hyperuricemia treatment as described in the classic traditional Chinese medical text Shang Han Lun. Recent studies have shown that common ginsenosides and rare ginsenosides (RGS) are the main active compounds in ginseng. RGS have higher activity and are less studied in the treatment of hyperuricemia. AIM OF THE STUDY: To determine whether RGS prevents and ameliorates potassium oxonate(PO)-induced hyperuricemia and concomitant spermatozoa damage in mice and the possible underlying mechanisms. MATERIALS AND METHODS: Potassium oxonate (PO, 300 mg/kg) induced hyperuricemia in mice via the oral administration of RGS (50, 100, or 200 mg/kg) or allopurinol (ALL, 5 mg/kg) for 35 days. Uric acid (UA) and xanthine oxidase (XO) levels were measured to assess the degree of histopathological damage in the liver, kidney, and testis, and renal creatinine (CRE), urea nitrogen (BUN), malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and inflammatory factor (IL-1ß) levels were measured to calculate the sperm density. Mechanisms were also explored based on blood and urine metabolomics and the gut microbiota. RESULTS: In this study, we demonstrated that RGS containing Rg3, Rk1, Rg6, and Rg5 could reduce serum UA levels, inhibit serum and hepatic XO activity, reduce renal CRE and BUN levels, further restore renal SOD and GSH activities, reduce the accumulation of MDA in the kidneys, and attenuate the production of renal IL-1ß. RGS was able to restore sperm density. Metabolomic analysis revealed that RGS improved sphingolipid metabolism, pyrimidine metabolism, and other metabolic pathways. 16S rDNA sequencing revealed that RGS could increase gut microbial diversity, restore the Firmicutes/Bacteroidetes (F/B) ratio, and adjust the intestinal microbial balance. Spearman's correlation analysis revealed a correlation between differentially metabolites and the gut microbiota. Lactobacillus and Akkermansia are the core genera. CONCLUSION: RGS can be a candidate for the prevention and amelioration of hyperuricemia and concomitant sperm damage. Its mechanism of action is closely related to sphingolipid metabolism, pyrimidine metabolism, and the modulation of gut microbiota, such as Lactobacillus and Akkermansia.

Flexible Tactile Sensors for 3D Force Detection.

As tactile force sensing has become increasingly significant in the field of machine haptics, achieving multidimensional force sensing remains a challenge. We propose a 3D flexible force sensor that consists of an axisymmetric hemispherical protrusion and four equally sized quarter-circle electrodes. By simulating the device using a force and electrical field model, it has been found that the magnitude and direction of the force can be expressed through the voltage relationship of the four electrodes when the magnitude of the shear force remains constant and its direction varies within 0-360°. The experimental results show that a resolution of 15° can be achieved in the range 0-90°. Additionally, we installed the sensor on a robotic hand, enabling it to perceive the magnitude and direction of touch and grasp actions. Based on this, the designed 3D flexible tactile force sensor provides valuable insights for multidimensional force detection and applications.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection.

OBJECTIVE: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. METHODS: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. RESULTS: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. CONCLUSION: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SIGNIFICANCE: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.

Introduction: In recent years, more and more attention has been paid to the visual fatigue caused by steady state visual evoked potential (SSVEP) paradigm. It is well known that the large-scale application of brain-computer interface is closely related to SSVEP, and the fatigue caused by SSVEP paradigm leads to the reduction of application effect. At present, the mainstream method of objectively quantifying visual fatigue in SSVEP paradigm is based on traditional canonical correlation analysis (CCA). Methods: In this paper, we propose a new SSVEP paradigm visual fatigue quantification algorithm based on underdamped second-order stochastic resonance (USSR) to accurately quantify visual fatigue caused by SSVEP paradigm in different working modes using single-channel electroencephalogram (EEG) signals. This scheme uses the fixed-step energy parameter optimization algorithm we designed, combined with the USSR model, to significantly improve the signal-to-noise ratio of the processed signal at the target characteristic frequency. We not only compared the new algorithm with CCA, but also with the traditional subjective quantitative visual fatigue gold standard Likert fatigue scale. Results: There was no significant difference (p = 0.090) between the quantitative value of paradigm fatigue obtained by the single channel SSVEP processed by the new algorithm and the gold standard of subjective fatigue quantification, while there was a significant difference (p < 0.001***) between the quantitative value of paradigm fatigue obtained by the traditional multi-channel CCA algorithm and the gold standard of subjective fatigue quantification. Discussion: The conclusion shows that the quantization value obtained by the new algorithm can better match the subjective gold standard score, which also shows that the new algorithm is more reliable, which reflects the superiority of the new algorithm.

Bionic intelligent ankle-foot prosthesis based on the conjugate curved surface.

Given the poor biomimetic motion of traditional ankle-foot prostheses, it is of great significance to develop an intelligent prosthesis that can realize the biomimetic mechanism of human feet and ankles. To this end, we presented a bionic intelligent ankle-foot prosthesis based on the complex conjugate curved surface. The proposed prosthesis is mainly composed of the rolling conjugated joints with a bionic design and the carbon fiber energy-storage foot. We investigated the flexibility of the prosthetic ankle joint movement, and the ability of the prosthetic foot to absorb ground impact during the gait cycle. Experimental results showed the matching of the ankle/toe position relationship of the human foot during simulated walking, which is helpful to realize the biomimetic motion of the human foot and ankle. It can also help therapists and clinicians provide better rehabilitation for lower-limb amputees.

The effect of motion frequency and sound source frequency on steady-state auditory motion evoked potential.

The ability of humans to perceive motion sound sources is important for accurate response to the living environment. Periodic motion sound sources can elicit steady-state motion auditory evoked potential (SSMAEP). The purpose of this study was to investigate the effects of different motion frequencies and different frequencies of sound source on SSMAEP. The stimulation paradigms for simulating periodic motion of sound sources were designed utilizing head-related transfer function (HRTF) techniques in this study. The motion frequencies of the paradigm are set respectively to 1-10 Hz, 15 Hz, 20 Hz, 30 Hz, 40 Hz, 60 Hz, and 80 Hz. In addition, the frequencies of sound source of the paradigms were set to 500 Hz, 1000 Hz, 2000 Hz, 3000 Hz, and 4000 Hz at motion frequencies of 6 Hz and 40 Hz. Fourteen subjects with normal hearing were recruited for the study. SSMAEP was elicited by 500 Hz pure tone at motion frequencies of 1-10 Hz, 15 Hz, 20 Hz, 30 Hz, 40 Hz, 60 Hz, and 80 Hz. SSMAEP was strongest at motion frequencies of 6 Hz. Moreover, at 6 Hz motion frequency, the SSMAEP amplitude was largest at the tone frequency of 500 Hz and smallest at 4000 Hz. Whilst SSMAEP elicited by 4000 Hz pure tone was significantly the strongest at motion frequency of 40 Hz. SSMAEP can be elicited by periodic motion sound sources at motion frequencies up to 80 Hz. SSMAEP also has a strong response at lower frequency. Low-frequency pure tones are beneficial to enhance SSMAEP at low-frequency sound source motion, whilst high-frequency pure tones help to enhance SSMAEP at high-frequency sound source motion. The study provides new insight into the brain's perception of rhythmic auditory motion.

A Precise Frequency Recognition Method of Short-Time SSVEP Signals Based on Signal Extension.

OBJECTIVE: Improving the Information Transfer Rate (ITR) is a popular research topic in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The higher recognition accuracy of short-time SSVEP signal is critical to improving ITR and achieving high-speed SSVEP-BCIs. However, the existing algorithms have unsatisfactory performance on recognizing short-time SSVEP signals, especially for calibration-free methods. METHOD: This study for the first time proposed improving the recognition accuracy of short-time SSVEP signals based on the calibration-free method by extending the SSVEP signal length. A signal extension model based on Multi-channel adaptive Fourier decomposition with different Phase (DP-MAFD) is proposed to achieve signal extension. Then the Canonical Correlation Analysis based on signal extension (SE-CCA) is proposed to complete the recognition and classification of SSVEP signals after extension. RESULT: The similarity study and SNR comparison analysis on public SSVEP datasets demonstrate that the proposed signal extension model has the ability to extend SSVEP signals. The classification results show that the proposed method outperforms Canonical Correlation Analysis (CCA) and Filter Bank Canonical Correlation Analysis (FBCCA) significantly in the measure of classification accuracy and information transmission rate (ITR), especially for short-time signals. The highest ITR of SE-CCA is improved to 175.61 bits/min at around 1s, while CCA is 100.55 bits/min at 1.75s and FBCCA is 141.76 bits/min at 1.25s. CONCLUSION: The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.

A Training-Free Infant Spontaneous Movement Assessment Method for Cerebral Palsy Prediction Based on Videos.

OBJECTIVE: Early diagnosis of infant cerebral palsy (CP) is very important for infant health. In this paper, we present a novel training-free method to quantify infant spontaneous movements for predicting CP. METHODS: Unlike other classification methods, our method turns the assessment into a clustering task. First, the joints of the infant are extracted by the current pose estimation algorithm, and the skeleton sequence is segmented into multiple clips through a sliding window. Then we cluster the clips and quantify infant CP by the number of cluster classes. RESULTS: The proposed method was tested on two datasets, and achieved state-of-the-arts (SOTAs) on both datasets using the same parameters. What's more, our method is interpretable with visualized results. CONCLUSION: The proposed method can quantify abnormal brain development in infants effectively and be used in different datasets without training. SIGNIFICANCE: Limited by small samples, we propose a training-free method for quantifying infant spontaneous movements. Unlike other binary classification methods, our work not only enables continuous quantification of infant brain development, but also provides interpretable conclusions by visualizing the results. The proposed spontaneous movement assessment method significantly advances SOTAs in automatically measuring infant health.

Enhancement of Time-Frequency Energy for the Classification of Motor Imagery Electroencephalogram Based on an Improved FitzHugh-Nagumo Neuron System.

Brain-computer interface (BCI) based on motor imagery (MI) electroencephalogram (EEG) has become an essential way for rehabilitation, because of the activation and interaction of motor neurons between the brain and rehabilitation devices in recent years. However, due to the discrepancies between individuals, the frequency ranges can be different even for the same rhythm component of EEG recordings, which brings difficulties to the extraction of features for MI classification. Typical algorithms for MI classification such as common spatial patterns (CSP) require multi-channel analysis and lack frequency information. With the development of BCI, the single-channel BCI system has become indispensable for simplicity of use. However, the currently available single-channel detection methods have low classification accuracy. To address this issue, two novel frameworks based on an improved two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract features of the single-channel MI. To evaluate the effectiveness of the proposed methods, this research utilized an open-access database (BCI competition IV dataset 2a), an offline database, and a 10-fold cross-validation procedure. Experimental results showed that the improved nonlinear FHN system can transfer the energy of noise into MI, thereby effectively enhancing the time-frequency energy. Compared with the traditional methods, the proposed methods can achieve higher classification accuracy and robustness.

Domain-Adaptive Prototype-Recalibrated Network with Transductive Learning Paradigm for Intelligent Fault Diagnosis under Various Limited Data Conditions.

In real industrial scenarios, intelligent fault diagnosis based on data-driven methods has been widely researched in the past decade. However, data scarcity is widespread in fault diagnosis tasks owning to the difficulties in collecting adequate data. As a result, there is an increasing demand for both researchers and engineers for fault identification with scarce data. To address this issue, an innovative domain-adaptive prototype-recalibrated network (DAPRN) based on a transductive learning paradigm and prototype recalibration strategy (PRS) is proposed, which has the potential to promote the generalization ability from the source domain to target domain in a few-shot fault diagnosis. Within this scheme, the DAPRN is composed of a feature extractor, a domain discriminator, and a label predictor. Concretely, the feature extractor is jointly optimized by the minimization of few-shot classification loss and the maximization of domain-discriminative loss. The cosine similarity-based label predictor, which is promoted by the PRS, is exploited to avoid the bias of naïve prototypes in the metric space and recognize the health conditions of machinery in the meta-testing process. The efficacy and advantage of DAPRN are validated by extensive experiments on bearing and gearbox datasets compared with seven popular and well-established few-shot fault diagnosis methods. In practical application, the proposed DAPRN is expected to solve more challenging few-shot fault diagnosis scenarios and facilitate practical fault identification problems in modern manufacturing.

Hybrid Triboelectric-Electromagnetic Magnetic Energy Harvester-Based Sensing for Wireless Monitoring of Transmission Lines.

Magnetic energy is an abundant and persistent form of energy radiating from various sources. Here, a hybrid triboelectric-electromagnetic magnetic energy harvester (HMEH) system consisting of a modified pendulum unit is proposed, interacting mechanically with two multilayered TENGs and remotely with Cu coils. Systematic studies are conducted on magneto-mechano-energy conversion from power transmission lines. The pendulum is made out of a thin PET plate, with two permanent magnets stuck at each side of the free end of the PET plate. Two multilayered TENGs (each of which has one layer fixed at the same angle while other layers are set free) are located at both sides of the pendulum unit. The coils and the magnets make up the electromagnetic generator (EMG). Multilayered TENGs are connected in parallel with the EMG (each unit is connected to an independent rectifying bridge), and it is possible to charge a 100 µF capacitor to 4.78 V within 55 s. The HMEH system is used to power up a thermometer continuously via a 47 µF capacitor. Furthermore, a design for a wireless early warning system for potential fire hazards due to overheating is realized, revealing potential applications for self-powered wireless monitoring of transmission lines.

Effects of Paradigm Color and Screen Brightness on Visual Fatigue in Light Environment of Night Based on Eye Tracker and EEG Acquisition Equipment.

Nowadays, more people tend to go to bed late and spend their sleep time with various electronic devices. At the same time, the BCI (brain−computer interface) rehabilitation equipment uses a visual display, thus it is necessary to evaluate the problem of visual fatigue to avoid the impact on the training effect. Therefore, it is very important to understand the impact of using electronic devices in a dark environment at night on human visual fatigue. This paper uses Matlab to write different color paradigm stimulations, uses a 4K display with an adjustable screen brightness to jointly design the experiment, uses eye tracker and g.tec Electroencephalogram (EEG) equipment to collect the signal, and then carries out data processing and analysis, finally obtaining the influence of the combination of different colors and different screen brightness on human visual fatigue in a dark environment. In this study, subjects were asked to evaluate their subjective (Likert scale) perception, and objective signals (pupil diameter, θ + α frequency band data) were collected in a dark environment (<3 lx). The Likert scale showed that a low screen brightness in the dark environment could reduce the visual fatigue of the subjects, and participants preferred blue to red. The pupil data revealed that visual perception sensitivity was more vulnerable to stimulation at a medium and high screen brightness, which is easier to deepen visual fatigue. EEG frequency band data concluded that there was no significant difference between paradigm colors and screen brightness on visual fatigue. On this basis, this paper puts forward a new index­the visual anti-fatigue index, which provides a valuable reference for the optimization of the indoor living environment, the improvement of satisfaction with the use of electronic equipment and BCI rehabilitation equipment, and the protection of human eyes.

Affleck-Dine Leptogenesis from Higgs Inflation.

We find that the triplet Higgs of the type-II seesaw mechanism can simultaneously generate the neutrino masses and observed baryon asymmetry while playing a role in inflation. We survey the allowed parameter space and determine that this is possible for triplet masses as low as a TeV, with a preference for a small vacuum expectation value for the triplet v_{Δ}<10 keV. This requires that the triplet Higgs must decay dominantly into the leptonic channel. Additionally, this model will be probed at the future 100 TeV collider, upcoming lepton flavor violation experiments such as Mu3e, and neutrinoless double beta decay experiments. Thus, this simple framework provides a unified solution to the three major unknowns of modern physics-inflation, the neutrino masses, and the observed baryon asymmetry-while simultaneously providing unique phenomenological predictions that will be probed terrestrially at upcoming experiments.

Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients.

Brain-computer interfaces (BCIs) are currently integrated into traditional rehabilitation interventions after stroke. Although BCIs bring many benefits to the rehabilitation process, their effects are limited since many patients cannot concentrate during training. Despite this outcome post-stroke motor-attention dual-task training using BCIs has remained mostly unexplored. This study was a randomized placebo-controlled blinded-endpoint clinical trial to investigate the effects of a BCI-controlled pedaling training system (BCI-PT) on the motor and cognitive function of stroke patients during rehabilitation. A total of 30 early subacute ischemic stroke patients with hemiplegia and cognitive impairment were randomly assigned to the BCI-PT or traditional pedaling training. We used single-channel Fp1 to collect electroencephalography data and analyze the attention index. The BCI-PT system timely provided visual, auditory, and somatosensory feedback to enhance the patient's participation to pedaling based on the real-time attention index. After 24 training sessions, the attention index of the experimental group was significantly higher than that of the control group. The lower limbs motor function (FMA-L) increased by an average of 4.5 points in the BCI-PT group and 2.1 points in the control group (P = 0.022) after treatments. The difference was still significant after adjusting for the baseline indicators ( ß = 2.41 , 95%CI: 0.48-4.34, P = 0.024). We found that BCI-PT significantly improved the patient's lower limb motor function by increasing the patient's participation. (clinicaltrials.gov: NCT04612426).

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering.

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland-Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (-0.095 logMAR), CCA (0.039 logMAR), and MSI (-0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.

Multi-scale noise transfer and feature frequency detection in SSVEP based on FitzHugh-Nagumo neuron system.

Objective. The steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals for brain-computer interfaces (BCIs) due to its excellent interactive potential, such as high tolerance to noises and robust performance across users. In addition, it has a stable cycle, obvious characteristics and minimal training requirements. However, the SSVEP is extremely weak and companied with strong and multi-scale noise, resulting in a poor signal-to-noise ratio in practice. Common algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of SSVEP detection under the multi-scale noise. Therefore, using linear methods to extract SSVEP with obvious nonlinear and non-stationary characteristics, the useful signal will be attenuated or lost.Approach.To address this issue, two novel frameworks based on a two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract feature frequency of SSVEP.Results.In order to evaluate the effectiveness of the proposed methods, this research recruit 22 subjects to participate the experiment. Experimental results show that nonlinear FHN neuron model can force the energy of noise to be transferred into SSVEP and hence amplifying the amplitude of the target frequency. Compared with the traditional methods, the FHN and FHNCCA methods can achieve higher classification accuracy and faster processing speed, which effectively improves the information transmission rate of SSVEP-based BCI.