Time Series Feature Extraction Research Articles

MDCNet: Long-term time series forecasting with mode decomposition and 2D convolution

Long-term time series forecasting is widely used in various real-world applications, such as weather, traffic, energy, healthcare, etc. Recently, time series decomposition techniques have been adopted in many mainstream forecasting models, such as the prevalent Transformer-based models, to help capture sophisticated temporal patterns and achieve success in several benchmark tasks. However, conventional decomposition algorithms are often based on simple operations or limited to specific fields, and therefore are not effective and applicable enough, especially for complex time series. In this paper, we propose Mode Decomposition and 2D Convolutional Network (MDCNet), a structure-simple yet effective forecasting architecture based on a more effective decomposition method and a multi-frequency time series feature extraction network with multi-scale 2D convolution. Specifically, we first introduce a Variational Mode Decomposition Block to discover intricate time patterns, which decompose time series into trend components and stationary modal components at different main frequencies. Then, we design a Trend Prediction Block and an Intrinsic Mode Functions Prediction Block to capture global correlation and hidden dependencies within different main frequencies, respectively. Furthermore, a Frequency Enhancement Module is designed to further reduce the impact of noise in long-term time series. Experiments on eight benchmark datasets show that MDCNet significantly reduces the error of the previous state-of-the-art method by 15.1% and 11.5% for multivariate and univariate time series, respectively.

Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition

Multi-Function Radars (MFRs) are sophisticated sensors with great agility and flexibility in adapting their transmitted waveform and control parameters. The recognition of MFR work modes based on the intercepted pulse sequences plays an important role in interpreting the functional purpose and threats of a non-cooperative MFRs. However, due to the increased flexibility of MFRs, radar work modes with emerging new modulations and control parameters always appear, and the supervised classification method suffers performance degradation or even failure. Unsupervised learning and clustering of MFR pulse sequences becomes urgent and important. This paper establishes a unified multivariate MFR time series feature extraction and clustering framework for MFR work mode recognition. At first, various features are collected to form the feature set. The feature set includes features extracted through deep learning based on recurrent auto-encoders, multidimensional time series toolkit features, and manually crafted features for radar inter-pulse modulations. Subsequently, several feature selection algorithms, combined with different clustering and classification methods, are used for the selection of an “optimal” feature subset. Finally, the effectiveness and superiority of the proposed framework and selected features are validated through simulated and measured datasets. In the simulated dataset containing 20 classes of work modes, under the most severe non-ideal conditions, we achieve a clustering purity of 73.46% and an NMI of 84.28%. In the measured dataset with seven classes of work modes, we achieve a clustering purity of 86.96% and an NMI of 90.10%.

Comparison of neural network architectures for feature extraction from binary black hole merger waveforms

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build datasets of 100 000 elements for each of four different waveform models (or approximants) in order to test how approximant choice affects feature extraction. Our choices include SEOBNRv4P and IMRPhenomPv3, which contain only the dominant quadrupole emission mode, alongside IMRPhenomPv3HM and NRHybSur3dq8, which also account for high-order modes. Each dataset element is injected into detector noise corresponding to the third observing run of the LIGO-Virgo-KAGRA (LVK) collaboration. We identify the temporal convolutional network architecture as the overall best performer in terms of training and validation losses and absence of overfitting to data. Comparison of results between datasets shows that the choice of waveform approximant for the creation of a dataset conditions the feature extraction ability of a trained network. Hence, care should be taken when building a dataset for the training of neural networks, as certain approximants may result in better network convergence of evaluation metrics. However, this performance does not necessarily translate to data which is more faithful to numerical relativity simulations. We also apply this network on actual signals from LVK runs, finding that its feature-extracting performance can be effective on real data.

Deep learning model for state of health estimation of lithium batteries based on relaxation voltage

Lithium-ion batteries have been widely used in many fields due to their high energy density, long cycle life and low self-discharge rate. However, most existing SOH estimation methods require a lengthy amount of time to determine features, the acquisition of multi-dimensional raw data is challenging, and the application scenarios are limited, making SOH estimation algorithms less practical in developing battery management systems (BMS). To face the challenge, this paper develops a new battery SOH estimation method and investigates the transferability of machine learning models under multiple operating conditions using transfer learning. First, features are extracted from the 15 s relaxation voltage using tsfresh, a Python package for time series feature extraction. Subsequently, four statistical features were obtained using a feature selection method based on the Pearson correlation coefficient. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) model is established and hyperparameters are adjusted using Bayesian optimization. The root mean square error (RMSE) and the mean absolute percentage error (MAPE) of battery SOH estimation on battery data set are 1.210 % and 1.225 %, respectively. Finally, based on transfer learning, the Bi-LSTM model is applied to battery datasets under multiple operating conditions, reducing the model's dependence on original training data while ensuring model accuracy. Considering the easy accessibility of short-term relaxation battery data, the battery SOH estimation method proposed in this paper is expected to facilitate the estimation of battery SOH under various operating conditions.

Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction

Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67 %, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators’ brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.

Asteroids co-orbital motion classification based on Machine Learning

ABSTRACT In this work, we explore how to classify asteroids in co-orbital motion with a given planet using Machine Learning. We consider four different kinds of motion in mean motion resonance with the planet, nominally Tadpole at L4 and L5, Horseshoe and Quasi-Satellite, building three data sets defined as Real (taking the ephemerides of real asteroids from the JPL Horizons system), Ideal and Perturbed (both simulated, obtained by propagating initial conditions considering two different dynamical systems) for training and testing the Machine Learning algorithms in different conditions. The time series of the variable θ (angle related to the resonance) are studied with a data analysis pipeline defined ad hoc for the problem and composed by: data creation and annotation, time series features extraction thanks to the tsfresh package (potentially followed by selection and standardization) and the application of Machine Learning algorithms for Dimensionality Reduction and Classification. Such approach, based on features extracted from the time series, allows to work with a smaller number of data with respect to Deep Learning algorithms, also allowing to define a ranking of the importance of the features. Physical interpretability of the features is another key point of this approach. In addition, we introduce the SHapley Additive exPlanations for Explainability technique. Different training and test sets are used, in order to understand the power and the limits of our approach. The results show how the algorithms are able to identify and classify correctly the time series, with a high degree of performance.

Dimensionality reduction and features visual representation based on conditional probabilities applied to activity classification

A large part of the information emitted by contemporary technological devices comes in the form of time series. The massive commercialization of these kinds of devices has made the study of time series feature extraction techniques acquire a vital relevance in last years. Two main things are essential when applying feature extraction techniques to time series: to reduce the dimensionality so it occupies the least amount of storage memory possible, and to make features that contain the relevant information regarding the nature of the data set and the goals to be achieved. For this purpose, we propose in this work a brand new technique called the State Changes Representation for Time Series (SCRTS), which relies on the relevant data associated with the conditional probabilities of the time series (also known in the literature as Markov model’s features), and the distribution of its values. This method is length-independent, which means that we can apply it to time series of different dimensions obtaining the same number of features for each one. Also, it provides a visual representation of the input data, so it is possible to interpret what makes a certain time series different from the other. After explaining how it works, we apply it to 3 different wearable accelerometer data sets. This algorithm reduces the original dimension of the time series considerably (in the best case from 5499 values to 31), having a good performance in the classification results (in the best chance with an accuracy of 98%).

A multi-agent reinforcement learning framework for optimizing financial trading strategies based on TimesNet

An increasing number of studies have shown the effectiveness of using deep reinforcement learning to learn profitable trading strategies from financial market data. However, a single-agent model is not sufficient to handle complex financial scenarios. To address this problem, a novel approach called Multi-Agent Double Deep Q-Network (Later called MADDQN) is proposed in this study, which reasonably balances the pursuit of maximum revenue and the avoidance of risk under the multi-agent reinforcement learning framework by innovatively employing two different agents represented respectively by two time-series feature extraction networks, TimesNet, and the Multi-Scale Convolutional Neural Network. Furthermore, to achieve a more generalized model suitable for different underlying assets, a mixed dataset containing three major U.S. stock indexes is collected. And the proposed model has been pre-trained in this dataset and subsequently refined for the specified asset. The results from experiments on five different stock indices show that the proposed MADDQN has an average cumulative return of 23.08%, outperforming the other baseline methods. Besides, the multi-agent model demonstrates its advantage in balancing the risk and revenue, in comparison with the single-agent models. Additionally, The generalization experiments confirm that the proposed MADDQN method after pre-training in the proposed mixed dataset could be stably transferred to the other underlying assets with a refinement. These findings indicate that the proposed framework not only achieves good performance in complex financial market environments but also is able to generalize robustly across different scenarios in various markets.

Three-hourly PM2.5 and O3 concentrations prediction based on time series decomposition and LSTM model with attention mechanism

In recent years, air quality has attracted wide attention from all over the world, among which the high concentration of particulate matter with an aerodynamic less than 2.5 μm (PM2.5) and ozone (O3) has a great adverse impact on human health and daily life. Previous studies on two pollutant predictions are overly concerned with model improvement but hardly focus on influence variable screening, and pollutant's time series features extraction and identification. To better improve the prediction accuracy and enhance the application of the model in practice, in the present study, a novel model RF-CEEMDAN-Attention-LSTM was proposed, which has three processes: (1) random forest (RF) screened out highly correlated influence variables; (2) complete ensemble empirical mode decomposition with adaptive noise (CEEMADN) method was adopted to decompose the PM2.5 and O3 concentration time series into multiple sub-time sequences; (3) the double hidden layer LSTM model with attention and dropout mechanism captured nonlinear relationships and dynamic changes of time series features. Three-hourly PM2.5 and O3 concentration in Chengdu were used to validate the effectiveness of the developed RF-CEEMDAN-Attention-LSTM model by comparing five other parallel models. The final results showed that the model not only had a better fitting effect on both PM2.5 and O3 than other comparable models in the entire timeline, but the model also had the highest R2 (PM2.5:0.916, O3:0.525) values for these two air pollutants at high concentration values.

Prediction method of peak carbon emission in power grid based on energy consumption elasticity coefficient

With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process.

Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data

BackgroundMajor depressive disorder (MDD) has a high rate of recurrence. Identifying patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the disabling effects of depression. MethodWe propose a novel feature extraction method that includes dynamic temporal information, and inputs the extracted features into a graph convolutional network (GCN) to achieve classification of recurrent MDD. We extract the average time series using an atlas from resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation was calculated between brain region sequences at each time point, representing the functional connectivity at each time point. The connectivity is used as the adjacency matrix and the brain region sequences as node features for a GCN model to classify recurrent MDD. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to analyze the contribution of different brain regions to the model. Brain regions making greater contribution to classification were considered to be the regions with altered brain function in recurrent MDD. ResultWe achieved a classification accuracy of 75.8 % for recurrent MDD on the multi-site dataset, the Rest-meta-MDD. The brain regions closely related to recurrent MDD have been identified. LimitationThe pre-processing stage may affect the final classification performance and harmonizing site differences may improve the classification performance. ConclusionThe experimental results demonstrate that the proposed method can effectively classify recurrent MDD and extract dynamic changes of brain activity patterns in recurrent depression.

Wind Turbine Blade Early Fault Detection With Faulty Label Unknown and Labeling Bias

In practical industrial applications, the need for a large number of accurately labeled training samples is a significant challenge for fault detection tasks. However, labeling all training samples is expensive and prone to labeling errors, especially for early fault detection of wind turbine blades. This paper proposes a labeling bias (LB) hypothesis. Assuming the labeler only needs to label parts of normal samples that are easy to judge, we design a probability ratio least-squares importance fitting (PRL-SIF) method based on variable homogeneity. Unlike other state-of-the-art positive unlabeled (PU) learning methods, PRL-SIF does not require knowledge of the class priors to achieve training. Furthermore, to better handle the multi-dimensional time-series data of wind turbines, we provide a data preprocessing method based on functional analysis to achieve time series feature extraction and dimensionality reduction. The effectiveness and robustness of the proposed method are verified on 23 real-world wind turbine datasets. Experimental results show that the proposed method can achieve nearly 90% accuracy while only labeling 20% of normal samples.

Trend and seasonality features extraction with pre-trained CNN and recurrence plot

GoogLeNet is a pre-trained Convolutional Neural Network (CNN) that allows transfer learning and has achieved high recognition rates in image classification tasks. A Recurrence Plot (RP) is an imaging method that depicts the recurrence of the state space system using coloured points and lines in 2D images. This work contributes to facilitating time series feature extraction by proposing a method that applies the GoogLeNet to time series images obtained with RP. The developed method is tested using simulated time series and selected time series from the M3 competition dataset. The results shows that the transfer learning approach allowed the extraction of business time series features by means of a GoogLeNet fine-tuned using 100 simulated time series. The combination of GoogLeNet and RPs outperforms the alternative and easier combination of GoogLeNet and plots of the time series and support the convenience of the RP transformation step. This application of deep learning techniques to business time series imaging offers opportunity for further developments.

Predicting ankle and knee sagittal kinematics and kinetics using an ankle-mounted inertial sensor

The purpose of this study was to develop a machine learning model to reconstruct time series kinematic and kinetic profiles of the ankle and knee joint across six different tasks using an ankle-mounted IMU. Four male collegiate basketball players performed repeated tasks, including walking, jogging, running, sidestep cutting, max-height jumping, and stop-jumping, resulting in a total of 102 movements. Ankle and knee flexion-extension angles and moments were estimated using motion capture and inverse dynamics and considered ‘actual data’ for the purpose of model fitting. Synchronous acceleration and angular velocity data were collected from right ankle-mounted IMUs. A time-series feature extraction model was used to determine a set of features used as input to a random forest regression model to predict the ankle and knee kinematics and kinetics. Five-fold cross-validation was performed to verify the model accuracy, and statistical parametric mapping was used to determine the difference between the predicted and experimental time series. The random forest regression model predicted the time-series profiles of the ankle and knee flexion-extension angles and moments with high accuracy (Kinematics: R2 ranged from 0.782 to 0.962, RMSE ranged from 2.19° to 11.58°; Kinetics: R2 ranged from 0.711 to 0.966, RMSE ranged from 0.10 Nm/kg to 0.41 Nm/kg). There were differences between predicted and actual time series for the knee flexion-extension moment during stop-jumping and walking. An appropriately trained feature-based regression model can predict time series knee and ankle joint angles and moments across a wide range of tasks using a single ankle-mounted IMU.

Personalized Machine Learning Approach to Estimating Knee Kinematics Using Only Shank-Mounted IMU

Knee kinematics is a valuable measure of knee joint function. However, collecting that data outside the clinic is difficult, especially with a limited number of wearable sensors and when you only use an ankle-mounted inertial measurement unit to estimate knee kinematics. Due to the cyclic nature of gait, it is possible to use machine learning to extract joint angles from only ankle-mounted sensors. This study aimed to use time-series feature extraction and a random forest regressor to generate a person-specific surrogate model for estimating knee joint flexion angles from a single-mounted inertial measurement unit above the ankle. Optical motion capture and inertial data from 10 healthy participants walking on a treadmill were collected to create 10 personalised surrogate models for estimating right knee flexion angles during gait. An additional 10 models were created for a leave-one-out analysis to test the generalisability of the models. Temporal cross-validation of the personalised models and a leave-one-out analysis was performed on the selected feature set. The personalised models achieved an average root mean square error of 2.45 ± 0.65 degrees (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.98) compared to a gold-standard optical motion capture. The generalised models achieved an average root mean square error of 6.77 ± 3.38 degrees (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.83) in the leave-one-out analysis. Time series feature-based personalised surrogate models could be used to accurately estimate knee kinematics by using a single ankle-mounted sensor. However, more data is required to train a generalised model using the presented method.

Time series classification based on convolutional network with a Gated Linear Units kernel

Time series data are ubiquitous in human society and nature, and classification is one of the most significant problems in the field of time series mining. Although it has been intensively studied, and has achieved significant results and successful applications, it is still a challenging problem, which requires capturing of multi-scale features of one-dimensional or multi-dimensional time series in variable length. In this paper, we propose a novel time series feature extraction block named Convolutional Gated Linear Units (CGLU), which is a combination of convolutional operations and Gated Linear Units for adaptively extracting local temporal features of time series. Combined with a temporal maxpooling block, it can extract global temporal features. To capture more diverse features, the Inception architecture is adopted to organize the CGLUs with different convolution kernel sizes, which result in the Convolutional GLU network. In order to evaluate the performance, we conduct extensive experiments on the UCR time series datasets (one-dimension) and UEA datasets (multi-dimension). Compared with baselines, our model obtains best results in terms of classification accuracy and training speed, which demonstrate effectiveness and efficiency of CGLUs and Conv-GLU network on time series classification tasks.

Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis

A transformer's acoustic signal contains rich information. The acoustic signal can be divided into a transient acoustic signal and a steady-state acoustic signal under different operating conditions. In this paper, the vibration mechanism is analyzed, and the acoustic feature is mined based on the transformer end pad falling defect to realize defect identification. Firstly, a quality-spring-damping model is established to analyze the vibration modes and development patterns of the defect. Secondly, short-time Fourier transform is applied to the voiceprint signals, and the time-frequency spectrum is compressed and perceived using Mel filter banks. Thirdly, the time-series spectrum entropy feature extraction algorithm is introduced into the stability calculation, and the algorithm is verified by comparing it with simulated experimental samples. Finally, stability calculations are performed on the voiceprint signal data collected from 162 transformers operating in the field, and the stability distribution is statistically analyzed. The time-series spectrum entropy stability warning threshold is given, and the application value of the threshold is demonstrated by comparing it with actual fault cases.

A CNN-Based E-Nose Using Time Series Features for Food Freshness Classification

In this article, a convolutional neural network (CNN)-based electronic nose system is proposed, which utilizes time series features to classify the freshness of food. The electronic nose is composed of three elements: a highly sensitive miniaturized micro-electromechanical systems (MEMS) metal–oxide (MOX)–semiconductor gas sensor array, a complementary metal–oxide–semiconductor (CMOS) integrated circuit, and a CNN-based classification unit. The gas sensors exhibit exceptional sensitivity, low power consumption, and a small size. The interface circuit for sensor readout is fabricated using a standard 180-nm CMOS process, with an area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.5\times1.5$ </tex-math></inline-formula> mm. The interface chip mainly consists of an analog-to-digital converter (ADC), an electrically erasable programmable read-only memory (EEPROM), an oscillator (OSC), power management, a heater driver, and a digital control circuit. Fixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}{(}{t}{)}$ </tex-math></inline-formula> exposures are taken at specific intervals under gas input and output conditions. Time series features are extracted from a diverse set of sensor signals, which allows the subtle differences in various food odors at different freshness levels to be identified. The incorporation of time series feature extraction extends the data features, resulting in enhanced classification accuracy with a limited number of sensors. An abstract odor map input to the CNN is formed by combining the steady-state and transient information of the gas sensor response. The final freshness classification accuracy of 20 types of foods is 97.3%. The accuracy is improved by 6.5% after the implementation of time series feature extraction, demonstrating the significance of instantaneous fluctuation information for classification accuracy.

Time Series Feature Extraction Using Transfer Learning Technology for Crop Pest Prediction

Following the rapid development of information and communication technology, and the huge amounts of data that have undergone explosive growth, artificial intelligence and machine learning have been used for predictive analysis in many fields. However, the prediction accuracy of these machine learning recognition models depends on the quality of the features selected for training. It is therefore very important to analyse characteristics that are meaningful and in line with the target variables as the training conditions for machine learning recognition models. In this paper, we analyse the correlation between features and target variables using the Pearson product-moment correlation coefficient, and integrate transfer learning technology for sequential feature extraction to enhance the prediction accuracy of a machine learning recognition model for the prediction of multiple crop pests and diseases as the performance verification target of the proposed method. The performance of our machine learning recognition model is compared with schemes in related work, and our approach is shown to increase the prediction accuracy by between 3% and 15%.

Research on migraine time-series features classification based on small-sample functional magnetic resonance imaging data

The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.