A Multiclass EEG Signal Classification Model using Spatial Feature Extraction and XGBoost Algorithm

Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhancement, and neurological rehabilitation. The BCI system depends on analyzing electroencephalogram (EEG) signals captured from the brain. Decoding these EEG signals is a complex process that combines multiple algorithms to extract meaningful information from these intricate and noisy signals. One of the most popular techniques is the Common Spatial Patterns (CSP), which helps preserve useful and sensitive information. This paper presents an optimized extension of the CSP model for extracting EEG data features in a multiclass setting using Riemannian geometry-based weighting. The use of weighting based on Riemannian geometry enhances the robustness of covariance matrix computation, thereby decreasing the influence of noise that can significantly distort the mean of covariance matrices in the traditional CSP method. The proposed approach is also extended by the integration of a multi-band filter bank, providing a more detailed examination of EEG signals. Three classifiers, Linear Discriminant Analysis (LDA), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery tasks. LDA achieves an accuracy of 80.40%, while MLP and RFC reach 80.02% and 80.90%, respectively. The results obtained using a majority vote combining the decisions of the three classifiers are 81.83% for accuracy and Recall, 82.74% for precision, and 81.87% for F1-score. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.

To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca2+ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.

Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm

In this study, the performances of Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms in agricultural crop classification were compared using multi-temporal Sentinel-2 images. The study area is located within an area of approximately 45* 45 km2 within the borders of Çukurova Plain. Within the scope of the study, agricultural crop pattern classification including corn, cotton, wheat, sunflower, sunflower, watermelon, peanut, and citrus trees, as well as double crop corn, soya, and cotton crops planted after wheat using RF, ANN, SVM, XGBoost algorithms with multi-time Sentinel-2 images of 2021. The study used parcels registered in the Farmer Registration System (FRS) as reference data. Before defining the parcels that are called Farmer Declaration Parcels (FDP) as ground truth data, pre-editing and rule-based deletion operations were performed, afterwards false and incorrect declarations were eliminated.10 bands (B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12) of Sentinel-2 imagery from 11 different dates and Enhanced Vegetation Index (EVI) were used. All classification results showed a high overall accuracy (OA) ranging from 85% to 92%. As a result of the classification using a total of 121 different features, the highest overall accuracy OA (92.14%) belongs to the XGBoost algorithm. XGBoost algorithm was followed by RF (89.15%), SVM (86.14%) and ANN (85.48%). In all algorithms, although cotton, maize and wheat gave the highest accuracy in the classification results, double crops (wheat-maize, wheat-soybean, wheat-cotton) especially those with very close phenological periods, could not reach very high accuracy values. As a result of the study, it was observed that crops at different phenological stages achieved high classification accuracy using all algorithms, while crops at very similar phenological stages lower classification results. With the methodology applied in this study, a large number of ground truth data were generated from FDP plots and used for classification.

Optimal Spatio-spectral Variable Size Subbands Filter for Motor Imagery Brain Computer Interface

Brain-computer interface (BCI) systems are proposed as a means of communication for locked-in patients. One common BCI paradigm is motor imagery in which the user controls a BCI by imagining movements of different body parts. It is known that imagining different body parts results in event-related desynchronization (ERD) in various frequency bands. Existing methods such as common spatial patterns (CSP) and its refinement filterbank common spatial patterns (FB-CSP) aim at finding features that are informative for classification of the motor imagery class. Our proposed method is a temporally adaptive common spatial patterns implementation of the commonly used filter-bank common spatial patterns method using convolutional neural networks; hence it is called TA-CSPNN. With this method we aim to: (1) make the feature extraction and classification end-to-end, (2) base it on the way CSP/FBCSP extracts relevant features, and finally, (3) reduce the number of trainable parameters compared to existing deep learning methods to improve generalizability in noisy data such as EEG. More importantly, we show that this reduction in parameters does not affect performance and in fact the trained network generalizes better for data from some participants. We show our results on two datasets, one publicly available from BCI Competition IV, dataset 2a and another in-house motor imagery dataset.

Electroencephalogram (EEG) based motor imagery (MI) brain-computer interface (BCI) has emerged as a promising tool for communication and control. Most MI classification methods use fixed-length time windows to intercept signals and perform subsequent analyses. However, the fixed-length time window interception method can not achieve optimal performance due to significant differences in the multiple imagining tasks of the same subject. In this paper, we present a novel interception method using long and short windows (LSWs). This method takes advantage of the subject’s motor imaginary strength at different times of the task to select specific time windows corresponding to the most salient features. The features corresponding to the selected time windows are used for the final MI classification. We compare the proposed LSW interception method with the fixed-length time window method on a public EEG dataset (BCI competition IV dataset 1) and a self-collected dataset. The results show that the classification accuracies are improved with the LSW interception method on both datasets. When using the support vector machine (SVM) classifier, the classification accuracy of common spatial pattern with the LSW method achieves 2.57% and 1.12% improvement on two datasets, respectively, and the classification accuracy of filter bank common spatial pattern (FBCSP) with the LSW method achieves 0.93% and 1.48% improvement, respectively. Among them, the classification accuracy of the LSW method with FBCSP and SVM is the highest, which is 93.43% and 91.12%, respectively. Compared with the traditional methods, this method significantly increases the classification accuracy and provides a new idea for researching the MI classification method in BCI.

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly improved for most of the subjects , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.

As the supply chain of the electric vehicle (EV) industry becomes increasingly complex and vulnerable, traditional supplier evaluation methods reveal inherent limitations. These approaches primarily emphasize static performance while neglecting dynamic future risks. To address this issue, this study proposes a comprehensive supplier evaluation model that integrates a hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework with the Extreme Gradient Boosting (XGBoost) algorithm, contextualized for the EV sector. The hybrid AHP-TOPSIS framework is first applied to rank suppliers based on multidimensional performance criteria, including quality, delivery capability, supply stability and scale. Subsequently, the XGBoost algorithm uses historical monthly data to capture nonlinear relationships and predict future supplier risk probabilities. Finally, a risk-adjusted framework combines these two components to construct a dynamic dual-dimensional performance–risk evaluation system. A case study using real data from an automobile manufacturer demonstrates that the hybrid AHP–TOPSIS model effectively distinguishes suppliers’ historical performance, while the XGBoost model achieves high predictive accuracy under five-fold cross-validation, with an AUC of 0.851 and an F1 score of 0.928. After risk adjustment, several suppliers exhibiting high performance but elevated risk experienced significant declines in their overall rankings, thereby validating the robustness and practicality of the integrated model. This study provides a feasible theoretical framework and empirical evidence for EV enterprises to develop supplier decision-making systems that balance performance and risk, offering valuable insights for enhancing supply chain resilience and intelligence.

An accurate prediction of carbon pricing is essential in carbon emission management, and also provides an important role for governments to formulate corresponding policies. However, due to the inherent complexity and dynamics of carbon price sequence, the effectiveness of different decomposition algorithms for carbon price remains to be tested. In addition, existing studies lack a systematic framework to explore the organic integration of external factors and secondary decomposition technology, and the feature processing of complex external factors still needs to be improved. In order to overcome the shortcomings of existing research, This paper presents a Variational Modal Decomposition(VMD) algorithm and a Complete Ensemble Empirical Mode Decomposition with Adaptive Second decomposition technology of Noise(CEEMDAN) decomposition algorithm, and extract the features of external factors by Extreme Gradient Boosting (XGBoost) algorithm. The HI-VMD-PE-CEEMDAN-XGBoost-Transformer model for predicting carbon price is constructed by the combined Transformer algorithm. Specifically, first, we use Hampel identifer(HI) to detect and rectify the anomalies in the original sequence. After applying Variational Mode Decomposition(VMD) decomposition algorithm, Permutation Entropy(PE) is utilized to reassemble the decomposed component. Quadratic Decomposition is performed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) algorithm. Then, the XGBoost algorithm is employed to extract features of external factors and screen key factors as predictive input variables. Finally, Transformer, which has stronger capability of large-scale data parallel processing, is selected as the prediction model to achieve a more scientific and effective carbon price prediction. The empirical analysis results based on EU carbon market data verify the validity and superiority of the proposed model in different forecasting scenarios.

Background: The number of patients diagnosed with rectal neuroendocrine tumors (R-NETs) is increasing year by year. An integrated survival predictive model is required to predict the prognosis of R-NETs. The present study is aimed at exploring epidemiological characteristics of R-NETs based on a retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database and predicting survival of R-NETs with machine learning.Methods: Data of patients with R-NETs were extracted from the SEER database (2000–2017), and data were also retrospectively collected from a single medical center in China. The main outcome measure was the 5-year survival status. Risk factors affecting survival were analyzed by Cox regression analysis, and six common machine learning algorithms were chosen to build the predictive models. Data from the SEER database were divided into a training set and an internal validation set according to the year 2010 as a time point. Data from China were chosen as an external validation set. The best machine learning predictive model was compared with the American Joint Committee on Cancer (AJCC) seventh staging system to evaluate its predictive performance in the internal validation dataset and external validation dataset.Results: A total of 10,580 patients from the SEER database and 68 patients from a single medical center were included in the analysis. Age, gender, race, histologic type, tumor size, tumor number, summary stage, and surgical treatment were risk factors affecting survival status. After the adjustment of parameters and algorithms comparison, the predictive model using the eXtreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in the training set [area under the curve (AUC) = 0.87, 95%CI: 0.86–0.88]. In the internal validation, the predictive ability of XGBoost was better than that of the AJCC seventh staging system (AUC: 0.90 vs. 0.78). In the external validation, the XGBoost predictive model (AUC = 0.89) performed better than the AJCC seventh staging system (AUC = 0.83).Conclusions: The XGBoost algorithm had better predictive power than the AJCC seventh staging system, which had a potential value of the clinical application.

Under adverse sea conditions, propeller ventilation caused by in-and-out water can decrease the reliability of the ship power grid and the lifespan of the propulsion shaft system. Predicting the development of propeller ventilation severity while identifying it can contribute to improving propeller ventilation control. In this study, the eXtreme Gradient Boosting (XGBoost) algorithm combined with a ship dynamics/control model is proposed as a propeller ventilation identification and prediction method. Meanwhile, the Pelican optimization algorithm (POA), particle swarm optimization (PSO), and genetic algorithm (GA) are applied to determine the optimal hyperparameters of the XGBoost algorithm. The results indicate that the method can effectively identify the current propeller ventilation state and predict whether a full ventilation state will occur after experiencing a partial propeller ventilation state. The comparison results indicate that the POA has a better optimization effect on the XGBoost algorithm for propeller ventilation identification and prediction. The method proposed in this study provides crucial technical support for the effective switching of propulsion control strategies for ship electric propulsion systems under adverse sea conditions.

This paper aims to propose an online relative complex permittivity measurement system at high temperature based on microwave interferometer. A ridge waveguide with a TE10 mode was used in which the sample was heated and measured simultaneously at a frequency of 2450 MHz, and the microwave interferometer is used to collect the amplitude and phase difference of the incident signal. The Extreme Gradient Boosting (XGBoost) algorithm trained by the corresponding simulation data is used to construct the inversion model to calculate the complex dielectric coefficient of the tested material. Besides, this paper uses linear regression algorithm (LR) to calibrate the measurement system in order to improve the measurement accuracy. The entire system was tested using different materials at room temperature, and the maximum error of the measurement accuracy is less than 8% compared to the theoretical data. The robustness of the entire system was also tested by measuring Macor materials up to 800 °C. This proposed method provides an effective way to understand the mechanism between microwaves and matter at high temperatures.

Renewable energy power prediction is crucial to economic dispatch and reliable operation of power systems. This paper proposes a wind power forecasting approach based on the Extreme Gradient Boosting (XGBoost) algorithm. XGBoost is not only an effective feature selection method but also an accurate forecasting approach. In order to avoid excessive manual interventions for hyperparameter tuning, the Tree-Structured Parzen Estimator (TPE) model is presented to optimize the hyperparameters of XGBoost. This forecasting strategy has been tested in a real wind farm in Spain, compared with Persistence and Support Vector Regression (SVR). The results show that the XGBoost algorithm has higher accuracy and is a novel effective approach for very short-term wind power prediction.

Background:Accurate prediction of ischemic stroke is required for deciding anticoagulation use in patients with atrial fibrillation (AF). Even though only 6% to 8% of AF patients die from stroke, about 90% are indicated for anticoagulants according to the current AF management guidelines. Therefore, we aimed to develop an accurate and easy-to-use new risk model for 1-year thromboembolic events (TEs) in Chinese AF patients.Methods:From the prospective China Atrial Fibrillation Registry cohort study, we identified 6601 AF patients who were not treated with anticoagulation or ablation at baseline. We selected the most important variables by the extreme gradient boosting (XGBoost) algorithm and developed a simplified risk model for predicting 1-year TEs. The novel risk score was internally validated using bootstrapping with 1000 replicates and compared with the CHA2DS2-VA score (excluding female sex from the CHA2DS2-VASc score).Results:Up to the follow-up of 1 year, 163 TEs (ischemic stroke or systemic embolism) occurred. Using the XGBoost algorithm, we selected the three most important variables (congestive heart failure or left ventricular dysfunction, age, and prior stroke, abbreviated as CAS model) to predict 1-year TE risk. We trained a multivariate Cox regression model and assigned point scores proportional to model coefficients. The CAS scheme classified 30.8% (2033/6601) of the patients as low risk for TE (CAS score = 0), with a corresponding 1-year TE risk of 0.81% (95% confidence interval [CI]: 0.41%–1.19%). In our cohort, the C-statistic of CAS model was 0.69 (95% CI: 0.65–0.73), higher than that of CHA2DS2-VA score (0.66, 95% CI: 0.62–0.70, Z = 2.01, P = 0.045). The overall net reclassification improvement from CHA2DS2-VA categories (low = 0/high ≥1) to CAS categories (low = 0/high ≥1) was 12.2% (95% CI: 8.7%–15.7%).Conclusion:In Chinese AF patients, a novel and simple CAS risk model better predicted 1-year TEs than the widely-used CHA2DS2-VA risk score and identified a large proportion of patients with low risk of TEs, which could potentially improve anticoagulation decision-making.Trial Registration:www.chictr.org.cn (Unique identifier No. ChiCTR-OCH-13003729).