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Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

Computers, Materials & Continua 2024年第6期79卷 4659-4681页

作者： Xin Xiong Jing Zhang Sanli Yi Chunwu Wang Ruixiang Liu Jianfeng He Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China College of Physics and Electronic Engineering Hanshan Normal UniversityChaozhou521041China Department of Clinical Psychology Second People’s Hospital of YunnanKunming650021China

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical *** methods such as Atomic Agglomerative Hierarchical clustering(AAHC),k-means clustering,Principal Component Analysis(PCA),and Independent Component Analysis(ICA)are limited by a fixed number of microstate maps and insufficient capability in cross-task feature *** these limitations,this study introduces a Global Map Dissimilarity(GMD)-driven density canopy k-means clustering *** innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG *** this advanced algorithm,the study analyzes the Motor Imagery(MI)dataset from the GigaScience database,*** findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions,with microstate C showing superior performance in all task *** imagined movement,microstate A was significantly *** with existing algorithms indicates a significant improvement in clustering performance by the refined method,with an average Calinski-Harabasz Index(CHI)of 35517.29 and a Davis-Bouldin Index(DBI)average of ***,an information-theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual *** utilizing the extracted microstate sequence parameters as features,the improved algorithm achieved a classification accuracy of 98.41%in EEG signal categorization for motor imagery.A performance of 78.183%accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a *** results demonstrate the potential of the advanced algorithm in microstate analysis,offering a more effective tool for a deeper understanding of the spatiotemporal features of EE

关键词： EEG microstate motor imagery k-means clustering algorithm gaus sian kernel function shannon entropy Lempel-Ziv complexity

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Towards an EEG-based brain-computer interface for online robot control

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第13期75卷 7999-8017页

作者： Li, Yantao Zhou, Gang Graham, Daniel Holtzhauer, Andrew Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Coll William & Mary Dept Comp Sci Williamsburg VA 23187 USA Mitre Corp Mclean VA USA

According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F-measure, and our results show that the algorithms can accurately classify the subjects' thoughts into corresponding action commands.

关键词： EEG BCI system k-means clustering algorithm Principal component analysis

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Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms

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IET COMPUTER VISION 2018年第8期12卷 1088-1095页

作者： Jaisakthi, Seetharani Murugaiyan Mirunalini, Palaniappan Aravindan, Chandrabose Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India SSN Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

Skin cancer is the most common type of cancer in the world and the incidents of skin cancer have been rising over the past decade. Even with a dermoscopic imaging system, which magnifies the lesion region, detecting and classifying skin lesions by visual examination is laborious due to the complex structures of the lesions. This necessitates the need for an automated skin lesion diagnosis system to enhance the diagnostic capability of dermatologists. In this study, the authors propose an automatic skin lesion segmentation method which can be used as a preliminary step for lesion classification. The proposed method comprises two major steps, namely preprocessing and segmentation. In the preprocessing step, noise such as illumination, hair and rulers are removed using filtering techniques and in the segmentation phase, skin lesions are segmented using the GrabCut segmentation algorithm. The k-means clustering algorithm is then used along with the colour features learnt from the training images to improve the boundaries of the segments. To evaluate the authors' proposed method, they have used ISIC 2017 challenge dataset and dataset, respectively.

关键词： biomedical optical imaging image segmentation image classification image filtering object detection skin cancer medical image processing pattern clustering graph theory image colour analysis automatic skin lesion segmentation method dermoscopic images GrabCut segmentation algorithm k-means clustering algorithm skin cancer dermoscopic imaging system lesion region skin lesion detection skin lesion classification visual examination automated skin lesion diagnosis system diagnostic capability enhancement preprocessing step segmentation step filtering techniques colour features ISIC 2017 challenge dataset PH2 dataset

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An integrated approach to the selection of municipal solid waste landfills through GIS, k-means and multi-criteria decision analysis

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ENVIRONMENTAL RESEARCH 2020年 185卷 109348-109348页

作者： Eghtesadifard, Mahmood Afkhami, Payam Bazyar, Arash Shiraz Univ Technol Fac Ind Engn Shiraz Iran

Selecting the proper site for disposing of solid waste is one of the serious environmental and public health concerns in metropolises. This multifaceted issue encompasses environmental, economic, social, geographical, technical, and legal criteria. Some of these criteria, however, are less influenced by experts' subjective judgments because they display scientific specifications. This study drew on a novel, integrated method for the selection of municipal solid waste (MSW) landfills in the Iranian metropolis, Shiraz;the study relied on the GIS and multi-criteria decision-making methods, which helped to reduce the number of comparisons in collecting experts' opinions, simplified the selection process, and enhanced the assessment method. The method proposed was regulated by DEMATEL and ANP. Primarily 13 criteria were identified in five groups through the Delphi method. Next, using the integrated method, the weight of each criterion was determined and was assigned to the corresponding layer in ArcGIS 10.5. By combining these layers through a fuzzy logic, the sites satisfying the disposal conditions were identified. The sites were then divided into six areas through the k-means clustering algorithm, while MOORA, WASPAS and COPRAS methods were used to discover the best sites based on their priorities. Finally, to confirm the reliability of the results, compare and verify them, and conduct sensitivity analysis on them, 13 scenarios were used.

关键词： Sustainable municipal solid waste management Landfill siting Geographic information system (GIS) Multi criteria decision analysis k-means clustering algorithm

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Underdetermined blind source separation method based on quantum Archimedes optimization algorithm

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APPLIED INTELLIGENCE 2023年第11期53卷 13763-13800页

作者： Gao, Hongyuan Zhang, Zhiwei Wang, Shihao Sun, Helin Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

The performance of the existing underdetermined blind source separation methods is very sensitive to the initial parameters, meanwhile, the existing setting or selection methods of initial parameters need to be improved. Consequently, an effective underdetermined blind source separation method is proposed in this paper to solve the above engineering problems. Based on the Archimedes optimization algorithm and quantum computing theory, this paper proposes a novel intelligent algorithm named quantum Archimedes optimization algorithm, which solves the objective functions for engineering problems. Then the optimal solution obtained through the quantum Archimedes optimization algorithm is used as the initial clustering centers of the k-means clustering algorithm to achieve mixing matrix estimation. In addition, the original initial estimation signal setting of the source recovery based on radial basis function network is converted into an initial solution in population for quantum Archimedes optimization algorithm. The optimal solution obtained through the quantum Archimedes optimization algorithm is used as the new initial estimation signal setting to achieve source recovery. The simulation results show that the proposed underdetermined blind source separation method has higher accuracy than previous methods. The proposed method that is more robust and applicable makes the setting and selection of initial parameters more reasonable so that the performance is no longer limited to the initial parameters.

关键词： Underdetermined blind source separation Quantum Archimedes optimization algorithm k-means clustering algorithm Radial basis function network

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Spatiotemporal evolution law and output prediction of construction waste in the People's Republic of China

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WASTE MANAGEMENT & RESEARCH 2022年第2期40卷 174-184页

作者： Zhao, Likun Liu, Yanqi Tian, Junsen North China Univ Technol Sch Civil Engn 5 Jinyuanzhuang Rd Beijing 100144 Peoples R China

Based on the relevant data of construction waste (CW) in the People's Republic of China (PRC) from 2010 to 2018, this study applied k-means clustering algorithm and grey prediction methods to systematically investigate the spatiotemporal characteristic distribution and provincial clustering of CW in the PRC, and predicted the annual output of CW in the next five years from the scientific perspective. Results showed that the annual output of CW in the PRC displayed an overall trend of "rising first and then falling" and "being high in the middle east and low in the northwest," and the areas with obvious agglomeration gradually spread from the west to the middle and eastern regions. The law of development was consistent with the goals of the Chinese government to promulgate urban agglomeration development policies, prefabricated building encouragement policies, and CW management regulations. In the next five years, the annual output of CW in the PRC will increase by a small margin. Thus, all aspects of CW resource management should be conducted in a planned and step-by-step manner.

关键词： Chinese construction waste spatiotemporal characteristics k-means clustering algorithm output prediction

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An approach to evaluate resource utilization in energy management systems

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2016年第9期11卷 855-860页

作者： Haeri, Abdorrahman Rezaie, kamran Iran Univ Sci & Technol Sch Ind Engn POB 16846-13114 Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Organizations implement management systems to achieve better performance in different functions. One important area of management systems is "energy management systems". ISO 50001: 2011 proposed a systematic framework for operation of an effective energy management system (EnMS). Resources should be used efficiently to obtain suitable performance. Thus, it is useful to evaluate resource utilization by considering the importance and efficiency of resources in processes. In this study, major functions of an EnMS are identified in accordance with ISO 50001. Then, for each function, processes and resources are specified and classified. Later, data are gathered, and appropriate measures are calculated. Finally, data analysis techniques are applied to propose strategic decisions for improvement. In addition, an oil refinery is considered as a case study.

关键词： Business process energy management system ISO 50001:2011 k-means clustering algorithm resource utilization

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Extracting the symmetry axes of partially occluded single apples in natural scene using convex hull theory and shape context algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第12期76卷 14075-14089页

作者： Niu, Leilei Zhou, Weicong Wang, Dandan He, Dongjian Zhang, Haihui Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

Accurate identification of apples partially occluded by branches and leaves is an urgent and key issue for a picking robot. The objective of this study was to detect the symmetry axes of partially occluded single apples accurately using the convex hull theory and Shape Context algorithm. Firstly, apple regions were obtained by using k-means clustering algorithm. Secondly, image pre-processing steps such as image binarization, hole filling, area opening and edge detection were applied. Thirdly, false contours were removed based on the convex hull theory to enhance the accuracy and stability of this method. Finally, the point matching relationship of each two contours and the two best symmetrical contours were found by using the Shape Context algorithm and Hungarian algorithm. Then the symmetry axes of apples were extracted using the matching point pairs. Least squares ellipses fitting algorithm and moment of inertia algorithm were used to compare with the presented algorithm. The angle difference between extracted symmetry axis and ideal symmetry axis for every method was computed, and the execution time of program as well. Ninety partially occluded single apple images were tested. The experimental results showed that the average angle error of the Shape Context algorithm were 7.72A degrees, 37.5 % of the ellipses fitting algorithm and 31.3 % of the inertia moment algorithm. And its average execution time is 1.86 s, 103 % of the ellipses fitting algorithm and 106 % of the inertia moment algorithm. In conclusion, it was feasible to use the proposed method to extract the symmetry axes of partially occluded apples.

关键词： Occluded apple Symmetric axis extraction Shape context Convex hull theory k-means clustering algorithm Threshold method

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Semi-supervised Rough Cost/Benefit Decisions

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FUNDAMENTA INFORMATICAE 2009年第2期94卷 233-244页

作者： Lingras, Pawan Chen, Min Miao, Duoqian St Marys Univ Dept Math & Comp Sci Halifax NS B3H 3C3 Canada Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China Jiao Tong Univ Minist Educ China Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China

Most of the business decisions are based on cost and benefit considerations. Data mining techniques that make it possible for the businesses to incorporate financial considerations will be more meaningful to the decision makers. Decision theoretic framework has been helpful in providing a better understanding of classification models. This study describes a semi-supervised decision theoretic rough set model. The model is based on an extension of decision theoretic model proposed by Yao. The proposal is used to model financial cost/benefit scenarios for a promotional campaign in a real-world retail store.

关键词： Rough sets Rough approximation Probability Decision theory Cost/benefit analysis k-means clustering algorithm

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Reversible data hiding method for multi-histogram point selection based on improved crisscross optimization algorithm

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INFORMATION SCIENCES 2021年 549卷 13-33页

作者： Weng, Shaowei Tan, Wenlong Ou, Bo Pan, Jeng-Shyang Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China Fujian Univ Technol Sch Elect Elect Engn & Phys Fuzhou 350118 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Traditional prediction error expansion (PEE)-based reversible data hiding (RDH) schemes focus on generating a sharp prediction error histogram (PEH) in order to increase embedding performance, but they neglect the influence of local complexity on embedding performance. When the PEH is partitioned into multiple sub-histograms by means of the local complexity, the embedding performance can be increased by excluding the embedding of data in the sub-histograms located in the texture regions. However, multiple sub-histograms also cause a problem about how to identify the optimal embedding points that can achieve the best visual quality for a given payload. Violent iteration is often used in RDH schemes to traverse all possible values of the embedding points in order to identify the optimal points. Therefore, we conclude that violent iteration is a very time-consuming method and that it leads to unacceptable computational complexity. The traditional multiple sub-histogram methods usually reduce the solution space (i.e., all possible combinations of the embedding points) to decrease the computational complexity. However, the solution that is obtained from the aforementioned methods may significantly deviate from the global optimal solution. Instead of shrinking the solution space by abandoning most solutions, we improve the crisscross optimization algorithm in order to search for the optimal solution in the global solution space. In this paper, the k-means clustering algorithm is used to classify all prediction errors into multiple categories according to the local complexity. Each category would generate a PEH. Subsequently, the problem of selecting the embedding points of multiple sub-histograms is transformed into a typical and multi-choice knapsack problem. The improved crisscross optimization algorithm is used to determine the approximate optimal solution. The experimental results showed that our scheme provided effective performance. (C) 2020 Elsevier Inc. All rights

关键词： Reversible data hiding Multiple prediction error histograms k-means clustering algorithm Multi-choice knapsack problem Improved crisscross optimization algorithm

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