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.
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.
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.
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.
The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant ...attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA‐variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well‐known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA‐related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA‐related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
Neuroscience applications of canonical correlation analysis (CCA) and its variants are systematically reviewed from a technical perspective. Detailed formulations, analytical and numerical solutions, current applications, and advantages and limitations of CCA and its variants are discussed. A general guideline to select the most appropriate CCA‐related technique is provided.
Artemisinin is a sesquiterpene lactone found in Artemisia cina having many medicinal properties. Artemisinin produced and stored in Glandular trichomes The purpose of this study was to determine the ...effect of shade 0%, 50% and 75% on the density and size of glandular trichomes and the content of artemisinin in A.cina tetraploid plants. The shade treatment significantly affected the density and size of glandular trichomes and the content of artemisinin in Artemisia cina tetraploid. 50% shade treatment showed the highest density of Glandular trichome, the width of glandular trichomes, and Artemisinin content. The results of the correlation analysis show that there is a very significant relationship between the density of glandular trichomes and the content of artemisinin.
With the integrated design and application of radar, communication and electronic reconnaissance, automatic modulation classification (AMC) is becoming increasingly significant in various ...applications, e.g. spectrum monitoring and cognitive radio, and has entered a stage of remarkable development. Recently, the AMC technology is suffering from complex and diverse signal types, low signal-to-noise ratio, poor algorithm robustness, and so forth. To cope with these flaws, this paper presents a modulation classification scheme based on deep feature fusion. In this work, the ResNeXt network is utilized to extract the distinctive semantic feature of the signal, and the gated recurrent unit (GRU) is employed to extract the time-series representation characteristic, respectively. Considering the complementarity between different features, a feature fusion model using discriminant correlation analysis (DCA) is proposed to fuse the output responses of the ResNeXt and GRU. The simulation results reveal that the proposed method achieves the superior performance, which is conducive to promoting the application of feature fusion in the AMC.
High mass resolution mass spectrometry provides hundreds to thousands of protein identifications per sample, and quantification is typically performed using label-free quantification. However, the ...gold standard of quantitative proteomics is multiple reaction monitoring (MRM) using triple quadrupole mass spectrometers and stable isotope reference peptides. This raises the question how to reduce a large data set to a small one without losing essential information. Here we present the reduction of such a data set using correlation analysis of bovine dairy ingredients and derived products. We were able to explain the variance in the proteomics data set using only 9 proteins across all major dairy protein classes: caseins, whey, and milk fat globule membrane proteins. We term this method Trinity-MRM. The reproducibility of the protein extraction and Trinity-MRM methods was shown to be below 5% in independent experiments (multi-day single-user and single-day multi-user) using double cream. Further application of this reductionist approach might include screening of large sample cohorts for biologically interesting samples before analysis by high-resolution mass spectrometry or other omics methodologies.
Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of ...multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the main problem of multi-view canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained. In addition, a non-linear extension of TCCA is presented. Experiments on various challenge tasks, including large scale biometric structure prediction, internet advertisement classification, and web image annotation, demonstrate the effectiveness of the proposed method.
Methane (CH4), a key gaseous product of coal–oxygen reactions that severely pollutes the atmosphere and is therefore a health hazard. The microcharacteristics of CH4 emission during coal spontaneous ...combustion (CSC) were investigated under five oxygen concentrations, six particle sizes, three coal ranks, and five heating rates. A novel temperature-programmed simulation was developed to investigate the combustion characteristics. In addition, microcharacteristics were studied using in situ Fourier transform infrared (FTIR) spectroscopy. Surface characteristics, namely the specific surface area and pore size distribution, were analysed. Four types of active functional groups were observed using the FTIR spectrometer. Moreover, the key functional groups that contributed to CH4 gaseous products were determined through grey correlation analysis. The results revealed that the carbonyl group was the most active functional group during low-temperature oxidation. Furthermore, the CH4 concentration increased gradually during low-temperature oxidation. During high-temperature oxidation, the CH4 concentration increased rapidly. The reaction of carbonyl compounds and aliphatic hydrocarbons was mainly responsible for CH4 formation.
•Four different factors that affect coal spontaneous combustion were investigated.•The analytical method of grey relationship was adopted to calculate the correlation between CH4 and 14 functional groups.•Key functional groups which generate CH4 in low- and high-temperature stage were found and elucidated.
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