Background: developmental dysplasia of the hip has an incidence of 3–5 out of 1000 children. Currently, only postnatal screening is available. Objective: to test the feasibility of a method based on Graf technique application at antenatal ultrasound in assessing the normal development of the hip in unselected term fetuses. Methods: a prospective cohort study in a single university tertiary hospital from January 2017 to January 2020. Single uncomplicated term pregnancies (37–40 weeks) attending our center for routine ultrasound were consecutively recruited for the purpose of the study. A 3D volume acquisition was launched on the coxofemoral joint of the fetus by a single expert operator, and offline analysis was then performed in the multiplanar mode by two operators  in order to measure the alpha and beta angles according to our modified Graf technique. Intra- and inter-observer variations were calculated. Reference charts for normal values of both angles were produced. Postnatal ultrasound was then performed to measure the Graf angles in newborns, confirming a normal development of the hip. Results: in the study period, 433 uncomplicated term pregnancies underwent 3D ultrasound for the assessment of the fetal hip. One case was subsequently excluded because of confirmed postnatal diagnosis of developmental dysplasia of the hip. The measurement of our modified Graf angles was feasible at prenatal ultrasound with a good reproducibility. The inter-rater and intra-rater reliability of both angles was substantial. Reference charts for normal values of both angles were produced. Conclusions: the evaluation of the coxofemoral joint in fetuses at term of gestation has never been attempted before. The Graf technique application, currently employed at postnatal ultrasound, may also be adapted to prenatal ultrasound with a substantial reproducibility. However, there was no evidence of a linear relationship between prenatal and postnatal alpha angles and beta angles. Further research is needed to establish if developmental dysplasia of the hip could be diagnosed antenatally. Developmental dysplasia of the hip is a spectrum of conditions that includes a mechanical instability of the hip joint due to an anomalous development of the acetabulum and proximal femur. The incidence of developmental dysplasia of the hip demonstrates high variability in literature due to the ambiguity of criteria for defining pathological hips, geographical differences, genetic predisposition, and cultural practices. The most recognized incidence of dislocatable hips and hips with severe or persistent dysplasia is 3 to 5 per 1000 children ,3. HowevThe etiology of developmental dysplasia of the hip is multifactorial, but established risk factors are breech presentation, female gender, positive family history, and being first born ,6. BreecThe left side is involved in 60% of the children, the right side in 20%, whereas 20% have bilateral involvement . This isEarly diagnosis of developmental dysplasia of the hip has been demonstrated to improve the outcome of affected babies. Residual sequelae of this condition are one of the leading causes of early hip osteoarthritis in adulthood . Indeed,An antenatal identification of fetuses with developmental dysplasia of the hip, or with an immaturity of the hip, that are at increased risk of developing this disorder after birth, would be useful in clinical practice. To our knowledge, no case of prenatal diagnosis of this pathology has been reported in literature and no normal range of fetal Graf angles are available for fetuses at term of gestation.The objective of this study was to test the feasibility of antenatal ultrasound, using the postnatal technique based on Graf angles, to assess the normal development of the hip in unselected term fetuses.This was a prospective cohort study. Before the experiment started, an institutional review board approval was obtained. From January 2017 to January 2020, all eligible single uncomplicated term pregnancies (37–40 weeks) attending our center for routine ultrasound were consecutively recruited for the purpose of the study. A 3D volume of the fetal hip was obtained in all cases when possible both fetal hips were insonated. Only the best volume acquisition was used for angles calculation in each fetus, irrespective of laterality.Inclusion criteria were age >18 or <48 years, gestational age from 37 week to 40 weeks, and single pregnancy in cephalic presentation or in breech presentation.2). Finally, cases with confirmed postanatal diagnosis of DDH were excluded from statistical analysis.Patients were excluded if any of the following conditions were present: evidence or suspicion of fetal structural or chromosomal abnormality, infectious diseases in pregnancy, twin pregnancy, maternal obesity  equipped with a multifrequency probe.The starting plane for 3D insonation was the midsagittal plane of coxofemoral joint; the “Y” sign was then visualized . When itAll examinations were performed by a single expert operator. The volume dataset of the coxofemoral articulation was acquired and then analyzed offline in the multiplanar mode by two operators  using the static volume contrast imaging (VCI) mode of the 4D view software . The 3D image was adjusted to achieve the midsagittal view of the coxofemoral articulation in the A-plane (“Y sign”), with the reference dot positioned in the middle of the iliac bone, at the level of the sling opening. On the magnified A plane, the alpha and beta angles were calculated according to our modified Graf technique ,16. The Offline volume analysis by either examiner never exceeded 5 min for each single dataset and led to calculation of both alpha and beta angles in all cases. All neonates underwent a postnatal ultrasound evaluation of the hips according to the Graf technique. This was performed by a blinded single expert operator. Only one fetus was excluded from the analysis of the percentage distribution of the alpha and beta angle because of the suspicion of developmental dysplasia of the hip at prenatal ultrasound, confirmed postnatally.n = 432) was large enough to build normal-based interval estimates of the study parameters.Owing to the central limit theorem, the size of the sample  estimates and 95% confidence intervals (CIs) on the basis of a single-rating, absolute-agreement, two-way random-effects model . Intra-rAll data were analyzed using the Stata 15 software .In the study period, 433 uncomplicated term pregnancies underwent 3D ultrasound for the assessment of the fetal hip. One case was subsequently excluded because of confirmed postnatal diagnosis of developmental dysplasia of the hip. Alpha and beta Graf angles were measured offline in the multiplanar mode by two experienced operators.The percentage distributions of the alpha angle and the beta angle are illustrated in r = −0.097; 95% CI −0.203 to 0.012) and beta angles . However, in the postnatal evaluation, alpha angles were systematically larger and beta angles systematically smaller.As shown in Developmental dysplasia of the hip is an important clinical problem, affecting infants and families worldwide. As this condition is the most common pediatric hip disorder causing considerable long-term debilitation if left untreated, a prenatal diagnosis of this condition would be useful, especially in settings where a postnatal universal screening is not available. Indeed, in most countries, only a targeted screening is routinely guaranteed, with the concrete possibility of missing some cases.The evaluation of the coxofemoral joint in fetuses at term of gestation has never been attempted before. The Graf technique application, currently employed at postnatal ultrasound, may be adapted to prenatal ultrasound with a substantial reproducibility.On the basis of our results, measuring these two angles at ultrasound appears feasible. The measurements obtained for both alpha and beta angles appeared fairly constant with a narrow interval of values. The intra-observer and inter-observer agreement for both seemed very good, with high intra-class correlation coefficient (ICC) and very small 95% CI.There was no linear relationship between prenatal and postnatal alpha angles and beta angles. However, in the postnatal evaluation, alpha angles were systematically larger and beta angles systematically smaller; this could be explained by the physiological immaturity of the fetal hip before birth.These results may have useful implications in clinical practice. Indeed, our measurement of alpha and beta angles may now be looked at as a reliable reference when assessing the normal anatomy of the coxofemoral joint in fetuses at term of gestation. We think that looking for the y-sign in fetuses at term is easy and not time-consuming—this could be done at routine ultrasound and does not require a more trained staff member. In this scenario, some more post-natal examinations would be performed, due to a prenatal suspicion of developmental dysplasia of the hip, but this is a little price to pay in order to reduce the long-term disabilities deriving from this undiagnosed condition.Further research is needed to establish if developmental dysplasia of the hip could be diagnosed antenatally using our modified Graf technique.To our knowledge, no study has ever attempted to evaluate the coxofemoral joint in a large number of fetuses. Moreover, our study provides reference ranges for fetal ultrasound hip measurements. The sonographic evaluation of coxofemoral joint in the prenatal period is feasible, with a technique that is easy to learn and not time-consuming. Indeed, the Graf angles can be easily measured at traditional 2D ultrasound. For the purpose of this study, a reproducibility study, we used 3D ultrasound in the multiplanar mode to measure alpha and beta angles, as this technique provides many advantages. Firstly, it permits a simultaneous visualization of the same structure in the three planes, making it easier to locate it in the space and to obtain a correct alignment of the structures involved in the angle measurements. Moreover, the enhancement volume contrast imaging mode now allows for the achievement of excellent quality images, often even better than those obtained with 2D ultrasound. Finally, it takes only a few seconds to acquire a good volume dataset, with the analysis of the angles performed offline by two independent operators measuring the same angles on the same volumes.A strength of our study is that the fetal position in the uterus is the same as the lateral decubitus position that newborns have to adopt during the postnatal acquisition (hips flexed at 30 to 45°). Moreover, the ultrasound acquisition of the Graf angles in fetuses is easy because fetuses near term are static.A limitation of our study could be that, in about 40% of our cases, we acquired only the proximal coxofemoral joint due to unfavorable fetal position. However, the purpose of the present study was to collect a large number of normal fetal hip 3D volumes in order to test the reproducibility of our modified Graf technique and to build reference range curves for normal fetuses at term, rather than spotting pathological cases. Further research is needed to establish if this technique might be used as an antenatal screening for developmental dysplasia of the hip; if this will be the case, a second scan might be arranged if it is not possible to acquire both junctions at the same time.Another possible limitation of our study is that it is not clear whether the maturity of the fetal hip is complete near term of gestation. According to Bialik et al. in 1999 [The evaluation of the coxofemoral joint in fetuses at term of gestation has never been attempted before. The Graf technique application, currently employed at postnatal ultrasound, may also be adapted to prenatal ultrasound with a substantial reproducibility, even if there was no evidence of a linear relationship between prenatal and postnatal alpha angles and beta angles. Further research is needed to establish if this method allows for an accurate diagnosis of developmental dysplasia of the hip in utero. However, if the evaluation of the fetal hips became part of routine ultrasound assessment in the third trimester, this could improve the possibility of diagnosis of developmental dysplasia of the hip, either directly or through a targeted postnatal screening for suspected cases. In this scenario, both fetal hips should be systematically analyzed.


Augmented reality (AR) is a new technology that increases users’ perception of the real world. The purpose of this study is to evaluate the efficacy and safety of augmented reality navigation system in treatment with craniofacial fracture reduction.This will be a single-center prospective randomized controlled trial. Twenty-two patients will be assigned to two groups of 11, and those with zygomaticomaxillary complex fractures will undergo preoperative three-dimensional CT modeling and have operational plans designed. The control team will use traditional optical navigation to perform the surgery, and the experimental team will use an AR navigation system. The primary outcome measures will be the accuracy of the key points of surgical area between the preoperational surgical plan and post-operation. The secondary outcome measures will be the blood loss, operation time, bone reduction time, hospital time, and complication rate. The findings obtained through this study are expected to evaluate efficacy and safety of the augmented reality navigation system in the treatment of zygomaticomaxillary complex fractures.This controlled trial of augmented reality navigation system in treatment with zygomaticomaxillary complex fracture reduction will clarify the efficacy and safety of this technology by measuring the accuracy of the key points of surgical area and blood loss, operation and bone reduction times, hospital stay duration, and complication rates. This is a single-center study, and the results are expected to promote the application of augmented reality in craniofacial fracture reduction to improve surgery accuracy and efficacy.ChiCTR1900022626. Registered on April 19, 2019.Chinese Clinical Trial Registry  The zygomaticomaxillary complex (ZMC) is an integral part of the facial skeleton. There are four articulations in the anatomy of ZMC including the zygomaticosphenoid suture, zygomatic arch with an articulation with the temporal bone, zygomaticomaxillary buttress, and the zygomaticofrontal suture. A bony fracture of the ZMC can damage one or more of the four articulations causing functional and cosmetic problems –5.In traditional ZMC fracture reduction surgery, surgeons usually conduct the preoperative surgical simulation mentally to determine the correct operational procedure and ensure the success of the operation. As a result, the quality of the surgical plan often depends on the surgeon’s own clinical experience and surgical skill, and it is difficult for every member of the surgical group to share that information about the surgical plan –10.Several techniques are used for accurate ZMC fracture reduction, but there are still many problems needed to be solved such as surgical navigation, virtual surgery planning, and a three-dimensional (3D) printing template –13. AugmRecent reviews indicate that AR could provide benefits that address the challenges of conventional navigation systems, such as hand-eye coordination and depth perception –19. The The purpose of this study is to (1) achieve real-time tracking and dynamic display using an optical marker plate based on the previous developed augmented reality system  and (2) This is a single-center prospective randomized controlled trial, performed in an academic hospital. The two parallel groups in this study include the experimental group patients receiving the AR assistant surgery and the control group to be treated with traditional navigation surgery. The allocation ratio of the two parallel groups is 1:1. The trial framework can be seen in Fig. https://www.chictr.org.cn/showproj.aspx?proj=38142)Patients with ZMC fracture reduction included in this study are from Shanghai 9th People’s Hospital, School of Medicine, Shanghai Jiao Tong University, which is an academic hospital. The protocol has been registered in the Chinese Clinical Trial Registry center (ChiCTR) with number of ChiCTR1900022626. (The inclusion criteria will include (1) preoperative and postoperative three-dimensional head computerized tomography (CT) scans, (2) unilateral zygomaticomaxillary complex fractures of less than 1 month history with no other surgical treatment, (3) no serious underlying diseases or contraindications, (4) no sex restriction with ages from 15- to 65-years-old, and (5) signed informed consent.The exclusion criteria include (1) compression fractures, fractures older than one month, (2) complex repair surgery requirement, (3) concurrent disease with contraindications for general anesthesia, and (4) no informed consent.The withdrawal criteria will include (1) failure to receive treatment as planned, (2) patient request to withdraw from the clinical study, and (3) those lost to follow-up.The trained investigator in the surgical team will introduce the trial to the patients who meet the inclusion criteria and obtain the written consent from the patients that agree to participate.Not applicable as no biological specimens would be collected.The control group is treated without the augmented reality for the surgical navigation during the reduction surgery which means that the surgical plan is decided mentally by the surgeon and only optical tracking is used in the operation procedure. This is the traditional treatment for craniofacial reduction surgery.All patients will undergo a 3D CT scan before surgery , and the data will be stored in DICOM format. All the digital material will be imported into the corresponding processing software . In the intervention group, two marker plates will be designed based on the coronal incision that can be fixed on the corresponding bone structure with fixation nails to simultaneously track the fracture block and the whole craniofacial bone  license from China Food and Drug Administration (CFDA). The Clinical Research Coordinator (CRC) is specialized to inform patients, to follow-up and collect relevant material. There is a statistician to assist the clinical data, which will be collected over 6 months for each patient to follow-up for complications. The principal investigator (PI) will check the process monthly and monitor trial progress. Every quarter, the Clinical Research Center/Office of Quality Management of Shanghai 9th People’s Hospital, School of Medicine, Shanghai Jiao Tong University, will conduct a data audit and verification of the program.There is no specific concomitant care or interventions prohibited during the trial. All the patients of the two groups will receive standard perioperative care for their craniofacial fracture.The whole surgical team will provide standard postoperative medical care for the participating patients after the trial.The primary outcome measures will be the accuracy of the key points of surgical areas between preoperational surgical plan and post-operation.All patients will undergo 3D CT examination 1 week after surgery. The preoperative and postoperative image data will be imported into 3D processing software . The preoperative and postoperative images will be registered, and the accuracy of the selected fracture points will be measured Fig. . To avoiThe secondary outcome measures will be blood loss, operation time, bone reduction time, hospital time, and rate of complications. The perioperative period outcome will be obtained from medical records. Any complications will be evaluated during the follow-up period Table .Table 1The primary endpoint is the success of post-operative 3D CT examination within seven days. The DICOM data will be transferred to the third-party measuring personnel, and all accuracy outcomes will be evaluated in the three-dimensional software.The second endpoint will be the success follow-up 6 months after operation. The complications during the follow-up period will be summarized as facial asymmetry, scarring, infection, epistaxis, hardware failure, neurapraxia, facial nerve palsy, temperature sensitivity, blindness, decreased visual acuity, diplopia, lid malposition, ectropion or entropion, corneal exposure or abrasion, enophthalmos, epiphora, and orbital dystopia [The participant timeline is shown in Table t-test. This sample size achieves 94.081% power to reject the null hypothesis of equal means when the population mean difference is 0.5 with a standard deviation for both groups of 0.3 and with a significance level (α) of 0.050 using a two-sided two-sample equal-variance t-test. To allow for a 10% potential dropout rate, the target sample size of this study will be set to 22.This experiment is a single-center prospective randomized controlled trial. Because this kind of experiment has not been carried out previously, the calculated number of samples mainly refers to the previously related published literature –24. WithThis trial is going to be performed in Shanghai 9th People’s Hospital, School of Medicine, Shanghai Jiao Tong University, which is an academic hospital. This hospital is well-known for its plastic and reconstruction surgery and there is a great amount of craniofacial fracture patients having surgery annually which guarantees the targeted number of participants will be enrolled.the permuted block randomized assignments list (1:1) has been used for grouping. One group is designated the control team and the other group was designated the experimental team. The random distribution list is developed and provided by a research statistician.In this randomized controlled study, All the parameters of the random distribution list and the random list itself were considered part of the blinded background and were stored in sealed envelopes. Before the blind is uncovered, all patients participating in the trial are kept confidential. Specific ID numbers are adopted. The identifiable information and study data is separated to ensure that the measurers are blind.During the trial, the operators are unable to design the blind method because he knew which way to use the intervention. Neither the enrolled patients nor the data measurer knew which group the patients were assigned to as part of a single blind trial.First, the investigators in the trial team will take responsibility of enrolling the participants who fulfill the inclusion criteria and get the consent. Then, the research statisticians conduct the randomized allocation and sent the randomized number of the patient. The surgeons will perform the surgery for the patient with or without the AR system according to the allocation. And the engineer who is blinded about the allocation measure the primary outcome.The operator is unable to design the blind method because he knew which way to use the intervention. The enrolled patients and the data measurer do not know whether they are undergoing surgery in the augmented reality assisted mode and conducted a single blind trial.This is a single blind trial, only the patients and measurer are blinded, and there is no circumstance that they need to be unblinded.The preoperative and postoperative image data will be imported into 3D processing software . The pre- and postoperative images will be registered, and the accuracy of the selected fracture points was measured.The primary outcome measures will be the accuracy of the key points of surgical site between the preoperational surgical plan and post-operation outcome. The post operational CT will be within 7 days after surgery, and this is the key outcome for the effectiveness of AR technology.For security measures including intraoperative blood loss, operation time, bone reduction time, hospital time, possible blood transfusion, and rates of complications, it will be arranged for surgeons to record relevant information after surgery within the follow-up period.Patients with craniofacial bone fracture care about the prognosis for it not only affects the function but also the esthetic issue. Besides, it is routine to record the patients’ contact information such as mobile phone number, the ID of Wechat, and the email. The patients also have the phone number of our center to contact our medical provider for any questions. In this way, we keep in close touch with the patients to insure the follow-up. In addition, some of the patients need to come back to our hospital for other treatment of combined injuries, like the ophthalmic issue. The medical team will provide standard pre- and post-operative care for trial patients to promote their retention for free. All patients will have 6 months follow-up for postoperative recovery and monitoring for complications and the PI will check the process and listen to the report of the investigator. Every quarter, the Clinical Research Center/Office of Quality Management of Shanghai 9th People's Hospital, School of Medicine, Shanghai Jiao Tong University, will conduct a data audit and verification of the program.The preoperative and postoperative image data will be imported into 3D processing software . The pre- and postoperative images will be registered and the accuracy of the selected fracture points measured.The primary outcome measures will be the accuracy of the key points of surgical site between the preoperational surgical plan and post-operation outcome. The post operational CT will be within 7 days after surgery, as the key outcome for the effectivity of AR technology.For security measures including intraoperative blood loss, operation time, bone reduction time, hospital time, possible blood transfusion, and rates of complications, this team will arrange surgeons to record relevant information after surgery within the follow-up period.The personal information of all the participants will be collected by trained investigators and stored in specific cabinets with limited access. All the digital data in the measurement software will be accessible only by password for the staff in charge of measuring the pre- and postoperative 3D data.Not applicable. No biological specimen is needed in this trial.N (%) and mean (± standard deviation) with median and interquartile range when appropriate. Comparisons between two groups will be made by Student’s t-test when the data was normally distributed. Fisher’s exact test will be used for the comparison of enumeration data between groups as the sample size was less than 40. P < 0.05 will be considered statistically significant.Primary and secondary outcomes will be analyzed and processed with SAS software, version 9.4 . All measurement data will be expressed as Not applicable. The study period is short, and no serious potential adverse event would occur.Not applicable. There is no subgroup analysis.In this trial, the analysis will be performed based on the intention to treat (ITT) principle. The ITT will also be used to deal with those who meet withdrawal criteria, including their demographic data. In case of missing data, the statistician will adopt the last observation carried forward (LOCF) to complete the analysis.https://www.chictr.org.cn/showproj.aspx?proj=38142. The data of the key findings and statistical code will be available on trial publication.The full protocol can be accessed by the website of This is a single-center trial without coordinating center. The trial staff consists of trained investigators taking responsibility for enrolment, clinical data collection, digital data measurement, and the surgical team providing the procedure and medical care. The PI of this trial will check the process and listen to the report of the trial from those investigators every month. The trial steering committee providing oversight of this trial is the Clinical Research Center/Office of Quality Management of Shanghai 9th People’s Hospital, School of Medicine, Shanghai Jiao Tong University, who will review the data and check the research progress every quarter.The Clinical Research Center/Office of Quality Management of Shanghai 9th People’s Hospital, School of Medicine, Shanghai Jiao Tong University, is the data monitoring committee of this trial; the staff and experts of the office will perform the audit and review the research data every quarter. It is independent from the sponsor and has no competing interests.According to the published literature on the application of AR in other kinds of surgeries, there is no reported adverse event related to this technique. The clinical doctor in the team will record and provide medical care if any complications occur in either group within the follow-up duration and report it to the ethical committee in a week.Every month, the PI will check the process and listen to the report of this trial. Every quarter, the Clinical Research Center/Office of Quality Management of Shanghai 9th People’s Hospital, School of Medicine, Shanghai Jiao Tong University, will conduct data audit and verification of program.Any amendments of the protocol will be submitted to the ethical committee and implemented once approved.The findings of this study will be disseminated through peer-reviewed publications and conference presentations and the privacy and interest of the patients will be protected. Data that would break the blind rules will not be released before completion of the trial. The PI, Dr. Gang Chai, will take the lead in publication and will be acknowledged in publications.This article presents a randomized controlled trial that aims to examine the efficacy of AR navigation for the treatment of ZMC fractures. The ZMC is a complicated anatomy containing four articulations, so the reduction surgery needs higher accuracy and stability to achieve satisfying functional and cosmetic outcomes. Augmented reality technology is a new computer-assisted surgery system based on virtual reality, featuring the combination of the real and virtual world and providing real-time navigation for the surgeon based on the 3D model. Current literature proves that it benefits the hand-eye coordination and depth perception in various kinds of surgeries. The results of this trial are expected to promote the application of AR technology in ZMC fracture surgery. Based on previous experimental studies on the application of AR technology in other kinds of surgeries, the sample size of this trial is small; therefore, the further step would be a larger sample size and multicenter trial. This trial will provide important data for the definitive trial design to gain clinical value and sustained improvements in the use of AR technology in ZMC fracture reduction.Protocol version = 1Date of register = April 19, 2019Date recruitment began = April 18, 2019Estimate completion date = May 2022


Solar energy is a critical component of the energy development strategy. The site selection for solar power plants has a significant impact on the cost of energy production. A favorable situation would result in significant cost savings and increased electricity generation efficiency. California is located in the southwest region of the United States of America and is blessed with an abundance of sunlight. In recent years, the state's economy and population have expanded quickly, resulting in an increased need for power. This study examines the south of California as a possibly well-suited site for the constructing large solar power plants to meet the local electricity needs. To begin, this article imposed some limits on the selection of three potential sites for constructing solar power plants . Then, a systematic approach for solar power plant site selection was presented, focusing on five major factors . This is the first time that the choosing by advantages (CBA) method has been used to determine the optimal sites for solar power plant construction, with the possible sites ranked as S2 > S1 > S3. The results were then compared with traditional methods such as the multi-criteria decision-making method. The findings of this study suggest that the CBA method not only streamlines the solar power plant site selection process but also closely aligns with the objectives and desires of the investors. In contrast, solar energy has the advantages of clean and low carbon emissions, which make it widely used in our life2. In recent years, solar energy is flourishing in different populated regions of the world to meet our energy needs and to preserve the environment.Historically, nonrenewable energy sources such as fossil fuels have been heavily relied upon to meet the energy requirements. However, its usage results in significant harmful gas emissions, which has a detrimental effect on the environment and the long-term growth of society3. In the European Union (EU), solar energy is being used on a large scale to reduce the total carbon dioxide emissions4. According to the California Energy Commission report, by implementing solar power in the energy grid, California would roughly triple its existing electrical grid capacity and maintain a record rate of renewable energy capacity expansion over the next 25 years to achieve the state's economy-wide climate goals5. In this context, increasingly more solar power plants will be installed in the next decade.Solar power generation is the most common way to use solar energy because of its ease of maintenance and low environmental impact. Solar power generation is predicted to significantly develop in the near future, particularly in industrial areas6. Thus, extending the lifespan of solar power plants and overcoming environmental hurdles posed by decommissioned plants at the end of their lifespan are popular topics of discussion. According to Domínguez, as more solar power plants are built, the amount of photovoltaic (PV) waste produced will dramatically increase7. Based on this, Farrell et al. reviewed and analyzed the recycling approaches of PV waste and assessed the potential energy value of waste PV modules to realize circular economy (CE)9. In the past few years, enormous progress has been made in the application and implementation of CE worldwide. The European Commission formulated a CE plan for the sustainable development of the EU in 201510. Many policymakers and stakeholders are seeking to apply CE to various fields, with the solar power industry leading the way. Solar power plant construction is the basis of realizing solar energy CE. This enhances coherence among environment, economy, and society, which creates a sustainable business environment for investors.However, increasing the number of solar power plants will be challenging. The lifespan of a solar power plant is roughly 25–30 years11. The existing literature mostly considers economic, environmental, and technological factors, but social factors, such as population density, are rarely mentioned14. With the rapid increase in the world population, factors related to social influence and human behavior are of great concern to decision-makers. Therefore, a comprehensive and connotative site selection model needs to be put forward to meet the site selection requirements. Herein, a new site selection model is proposed based on a comprehensive research background, considering economy, technology, society, geography, and environment.To maximize the CE benefits of the solar power industry, the optimal site must be found for the construction of solar power plants, which requires a balance of economy, society, environment, and climate, and is regarded as a multi-criteria decision-making (MCDM) problem16. As a result, when faced with high-cost investments, stakeholders need to analyze the costs separately to make the risks of the projects transparent17. The investment cost of solar power plants is 4739 $/kW, while the investment cost of concentrating solar power plants is 5213–6672 $/kW in the United States of America18. The construction cost of the Crescent Dunes Solar Energy Project was $1 billion in 201519. Therefore, due to the high construction costs, the investment cost in the solar power plant construction needs to be considered and analyzed to make these projects profitable for the investors. Existing studies treat cost factor by comparing its importance with other factors, which do not highlight the importance of cost and makes cost insensitive to the impact of site selection21. To fill this research gap, this paper considers cost as an independent factor in the process of solar power plant site selection to reflect the value of cost and to maximize investors’ return on investment.Nevertheless, the realization of CE is affected by the investment decisions made by stakeholders considering the high costs of solar power plant construction. For investors, projects will not be selected that have low investment returnsThis paper created a comprehensive and methodical scheme for solar power plant site selection, which includes five basic factors and corresponding sub-factors: economy, technology, society, geography, and environment. Then, considering the high investment cost of solar power plant construction, this paper separates the cost from other factors to maximize investors' return on investment. The scheme is applied to support the site selection of solar power plants in California.The CBA method is firstly used in the site selection for large solar power plants, and it provides a new solution for adequate decision-making.In order to provide a comprehensive research background and reflect the value of cost, a new choosing by advantages (CBA) method is applied in this paper. The main contributions of this paper are as follows:This paper primarily aims to propose a valuable and meaningful scheme of solar power plant site selection to provide technical support for the realization of solar energy CE. The remainder of the study is divided into the following sections: “24. In recent years, the decision-making problems have gradually developed into complex MCDM problems, which are often accompanied by the subjectivity of decision-makers and the uncertainty of information.In this section, the existing research on the current MCDM methods and their application to the optimal site selection of solar power plants are briefly reviewed. MCDM is a well-known decision-making approach in operations research that encompasses a variety of techniques. Tirkolaee and his team have used MCDM to solve a series of decision-making problems, including supplier selection in the healthcare industry, enterprise business plan decision-making, and the optimal allocation of energy25. Sahu et al. proposed a method based on picture fuzzy set and rough set to solve the decision-making problem26. However, these methods cannot deal with soft multiset scenarios. To overcome this challenge, the concept of soft multiset and soft multiset topology are extended by Riaz to solve the MCDM problems27.Based on this, the fuzzy theory and concept have been developed to meet the decision-making requirement. Ali et al. proposed a complex interval-valued Pythagorean fuzzy set for green supplier chain management selection28. TOPSIS is an MCDM method based on the distance between positive and negative ideal solutions . Rani et al. extended fuzzy TOPSIS with the new divergence measures to select renewable energy sources29. At present, TOPSIS has proven to have good applicability in various fields, especially in site selection30.Progressively more MCDM methods have been developed by combining with the fuzzy concept and theory. Mishra et al. combined the technique for order preference by similarity ideal solution (TOPSIS) method with intuitionistic fuzzy weighted measures to solve the decision-making problem of the investment policy choice31. Uluta et al. further extended MARCOS with correlation coefficient and standard deviation (CCSD) and indifference threshold-based attribute ratio analysis (ITARA) methods to the logistics system32. However, the main limitation of this method is that it is difficult to express the evaluation criteria correctly through explicit numerical values. Therefore, Brkovic et al.33 presented an integrated full consistency method–MARCOS model, and Celik et al.34 integrated the best–worst method (BWM), MARCOS, and interval type-2 fuzzy sets to avoid this limitation. From the perspective of application, the MARCOS method’s applicability in the field of site selection has not yet been proven.The measurement alternatives and ranking according to compromise solution (MARCOS) method was developed by Stevic et al. based on the idea of TOPSIS36. Wang et al. developed an improved MABAC method based on the q-rung orthopair fuzzy set (Q-ROFS) environment. However, due to the limited practical use of Q-ROFS and MABAC, this combination method may not be appropriate for use in real-life problems37. Similar to the TOPSIS, MARCOS, and MABAC methods, the multi-attribute ideal–real comparative analysis (MAIRCA) and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods were combined with fuzzy concept, such as fuzzy analytic hierarchy process (FAHP)-VIKOR38 and FAHP-MAIRCA methods39.Multi-attributive border approximation area comparison (MABAC) is an area boundary approximation method, and Pamucar et al. extended different MABAC methods to solve different decision problem40. The PROMETHEE method with intuitionistic fuzzy soft sets has been extended to solve the decision-making problems with intuitionistic fuzzy information. The PROMETHEE method has great advantages when decisions to be made by experts are influenced by their respective areas of expertise, so it has been widely used for site selection42.Different from TOPSIS, MARCOS, MABAC, MAIRCA, and VIKOR, preference ranking organization method for enrichment evaluation (PROMETHEE) is an outranking method. Researchers extended the PROMETHEE method to different scenarios. A fuzzy PROMETHEE method combined with trapezoidal fuzzy interval numbers has been applied to the automobile industry43, PROMETHEE44, and VIKOR45 have been proven to have good performance in the field of solar power plant site selection. However, in the application of TOPSIS, the factors of solar power plant site selection are not fully considered such as geographical disasters, population density, and visual impact43. In PROMETHEE44, payback period, population density, and policies are not taken into account45. Factors such as geographical disasters and policies are also not mentioned in VIKOR. In addition, the cost is not considered as a single component but compared with other factors in the TOPSIS, PROMETHEE, and VIKOR methods, which hides the true value of cost and reduces its influence on the decision-making results.The main form of the current MCDM methods is in combination with fuzzy concept, weight determination methods, and ranking methods. This proves that the current MCDM methods are mature and can be effectively applied to decision-making involving a large number of fuzzy and uncertain factors and information, such as the site selection for solar power plants. TOPSIS46. It can solve the MCDM problems and separate cost from other factors in the process of decision-making to fully ensure the real value of cost. The CBA method has been successfully applied in the architecture, engineering, and construction industry and has proven to be better than other traditional approaches49. The advantages of the CBA method are as follows28: (1) It provides a more transparent environment for the decision-makers. (2) It can be closely related to the context of the project, reducing the time for decision-makers to reach consensus. (3) Cost factors are considered separately to ensure its importance on decision-making results. Therefore, the CBA method is adopted for the optimal site selection for solar power plants in this study. Table To overcome the challenges of the TOPSIS, PROMETHEE, and VIKOR methods in solar power plant site selection, this paper proposes a more comprehensive and meaningful scheme that incorporates CBA method and a solar power plant model involving economic, technological, geographical, environmental, and social factors. This scheme can also maximize the interests of investors and the CE of solar power projects based on the CBA method. The CBA method is a lean decision-making method built by Suhr in 1999 that supports sound decision-making using alternative advantage comparisonsVisual impact The construction of solar power plants would have an effect on the daily life of animals and humans52. To maintain the long-term viability of the ecosystem, the visual impact of solar power plants must be considered during the design stage.Solar irradiation potential This is clearly the key indicator determining whether solar power plants can be built at a particular site. Solar power plant’s ability to produce energy and save money is directly impacted by the amount of available solar energy. With higher amount of solar radiation being available, more electricity can be generated, making the electricity grid more efficient53.Land type In some places, the land type and availability might be a critical factor in determining the site for solar power plant construction. Numerous countries have regulations regarding the types of land that can be used for solar power projects. Generally, it is preferable to employ construction land rather than agricultural land, as this would contravene the principle of sustainable growth.Geological disaster This is a critical geographical factor in the development of solar power plants. If an area is prone to geological disasters, such as tsunamis and earthquakes, investors will encounter significant risks, and there is no value in installing solar power plants in such areas.Policy It is critical to consider local policies for site selection. Solar energy generation is expensive due to technical constraints. When a country or municipal government reduces taxes while increasing energy prices, the investment rate increases, relieving the financial pressure on investors.Social benefit Solar power plants are built to meet the interests of investors while also positively contributing to society. They will assist in promoting local businesses and creating jobs, thereby impacting local education and culture54.Public attitude The development of large solar power plants is a massive and time-consuming endeavor. They often have detrimental effects on nearby inhabitants in terms of noise for example. It is necessary to perform extensive research to ascertain whether the local populace supports solar power plant construction.Local development planning This serves as the foundation for the investment and commercial decision-making. If the local economy and social system have remained stagnant and saturated, the viability and hazards of investing in solar power plants must be evaluated.Payback period This is a critical factor to examine when determining whether a project is worth investing in, and it is also a benchmark for decision-makers when determining a project’s profitability. When selecting a site for solar power plants, a project with a lengthy payback period is inappropriate and should not be prioritized.Investment cost This is a critical factor when undertaking any project. It weighs the project’s expenses and benefits, and its appropriate consideration would lead to a cost-effective and dependable solution. The investment cost primarily encompasses the costs for land acquisition in this paper.Rainfall Rainfall may damage solar panels and other construction equipment, reducing their lifespan. Solar power plants should be constructed with extreme caution in places prone to excessive precipitation.Temperature Temperature can affect the longevity of solar power plants. Increased temperature can reduce the efficiency of solar energy conversion devices, resulting in decreased output55. When the average temperature is maintained at a steady and acceptable level, solar power plants can operate at maximum capacity.Humidity Increased humidity results in less solar radiation, lowering the performance of solar energy conversion, increasing the cost of power generation56.Distance to roads/substations The technical strategy must account for the distance between solar power plants and roads and substations. Solar power plants built near transformer substations will help reduce equipment transportation costs and enable easier construction of new infrastructure.Population density This illustrates how metropolitan systems evolve. The population distribution and density are also critical variables in the solar power plant site selection process.Following a comprehensive review of the relevant literature and consultation with industry experts, this paper suggests 16 essential site selection factors. However, at some point throughout the site selection process, the characteristics of factors may have an effect on the output’s accuracy. To ideally solve this problem, the factors considered in this study can be classified as positive or negative, based on whether or not they contribute positively solar power plant production enhancement, respectively. Visual impact, solar irradiation potential, land type, geological disaster, policies, public attitude, and local development planning are considered beneficial criteria; in contrast, payback period, investment cost, rainfall, temperature, humidity, distance to roads, distance to substations, and population density are considered detrimental criteria. This treatment would advocate for simplifying the MCDM model and outlining the CBA model’s decision-making rules. The justification and explanation for the selection of each factor is discussed in greater detail below:All of the abovementioned factors were determined with the assistance of experts and relevant institutions from around the world to bolster the viability of the site selection system and data dependability. Experts include local governments, government agencies, consultants, renewable energy specialists, project managers, quantity surveyors, engineers, architects, scientists, and stakeholders. Their knowledge and abilities ensure the logic and dependability of the system.This research evaluates the economic, technological, environmental, geographical, and social factors of the study region, as well as the potential for solar power generation growth, to maximize the benefits from a solar power plant. A precise approach for the site selection of solar power plants has been developed.Figure Step 1:Create a site selection model based on the 16 factors and suggest some constraints to help define possible site alternatives .Step 2:Collect and evaluate relevant data for each site alternative in accordance with the site selection method.Step 3:Determine the optimal site using the CBA model.This approach would improve the precision and objectivity of the site selection process’s outcome. It must be noted that due to the low slope angle of the land in the study field, the slope and orientation of the land are not included in this research.This study focused on the southern California counties of San Bernardino and Riverside Fig. , which a58:Determining possible site alternatives. In this study, three possible site alternatives  are ultimately produced by imposing some constraints on the investigation. These are the site alternatives that are used to conduct the evaluation.Defining criteria and factors. “Enumerating the characteristics of each site alternative. This process involves the experts and stakeholders developing choice rules for each criterion and factor, as well as summarizing the qualities of each site alternative.Assessing advantages of each site alternative. This step requires the stakeholders to evaluate the merits of each site alternative based on the specified criteria and factors, which should be a straightforward undertaking.Deciding the importance of each advantage. The decision-makers should prioritize each advantage. Participants used a scale ranging from 1 to 100 to assign varying degrees of importance. To begin, the “most critical advantage” should receive a score of 100. The following goal is to utilize the “most critical advantage” as a baseline against which the remaining advantages can be compared. The final stage is to determine each site alternative’s total importance of advantages (IofAs).Choosing the best site alternative. The cost of each site alternative is calculated to obtain the cost–IofAs curve. The site alternative that gives the most value for money should be chosen by the stakeholders and decision-makers.CBA’s tabular approach is utilized for solar power plant site selection. As illustrated in Fig. In contrast to the standard MCDM method, the CBA method places a premium on the relative advantages of the factors rather than their relative importance. To confirm the accuracy of the data and the method's viability, experts from around the world were enlisted to define the criteria and weigh the relative merits of each choice. As a consequence, 15 decision-making factors and criteria . The fundamental reason for this is that the investment cost was factored into the TOPSIS and PROMETHEE model evaluations at the beginning, and as a result, S3 scored better than S1 due to its superior performance of other factors. In other words, the disadvantage of S3’s investment cost is outweighed by its other benefits. As a result, when traditional MCDM methods are used for decision-making, the investment cost is weighed against other factors. Unlike the typical MCDM model, the CBA model incorporates the predicted investment cost of each choice as an independent factor to constrain the result. That is, despite the fact that S1 performed brilliantly in this study and achieved a high score in a multitude of areas, due to the high estimated investment cost, investors and decision-makers will not select it.In addition, the CBA method is more sensitive to cost changes than the traditional MCDM methods. To facilitate comparison, the same cost was determined for all site alternatives as a baseline to analyze the changes in the CBA, TOPSIS, and PROMETHEE results Table . This paFor the TOPSIS and PROMETHEE methods, when the results changed, the cost of S1 increases ranged from 15 to 20% and 25 to 30% respectively. Moreover, the results remain the same when the initial cost base of the site alternative increases (case 2). This indicates that CBA method makes the result more sensitive to the change in cost. The main reason is that the advantages of S1 in other aspects make up for S1's disadvantages in cost to varying degrees in the TOPSIS and PROMETHEE methods. Therefore, S1 will not be considered the worst option unless its cost increases so dramatically that the cost disadvantage outweighs the other advantages. For the CBA method, cost is an independent parameter and will not be interfered by other factors, which fully reveals the impact of the cost on the results. As a result, when evaluating projects with high cost, the results of the CBA method will enable decision-makers to fully consider the cost factor to reduce project risks.Scenarios A: Modify the IofAs of the factors associated with the social factors proportionately while maintaining the IofAs of the other factors.Scenarios B: Modify the IofAs of the factors associated with the environmental factors proportionately while maintaining the IofAs of the other factors.Scenarios C: Modify the IofAs of the factors associated with the economic factors proportionately while maintaining the IofAs of the other factors.Scenarios D: Modify the IofAs of the factors associated with the technological factors proportionately while maintaining the IofAs of the other factors.Scenarios E: Modify the IofAs of the factors associated with the geographical factors proportionately while maintaining the IofAs of the other factors.To ensure the reliability of the CBA results and to reduce the influence of decision-makers' subjectivity on the results, five scenarios are designed to investigate how the results fluctuate when one factor's advantages change in this study. We altered the relative advantages of the social, environmental, economic, technological, and geographical factors. The details of the scenarios are as follows:Tables This paper begins with the discussion of CE and considers that choosing an optimal site for solar power plants is an important way to promote the CE of renewable energy. Considering the high cost for the construction of solar power plants, this paper separates the cost from the other factors in the process of solar power plant site selection to provide investors with the maximum investment return. Considering the complexity of solar power plant construction, this study proposes a scheme that incorporates the CBA method and a solar power plant model involving economic, technological, geographical, environmental, and social factors to provide technical support for optimal site selection in California. This scheme also provides a new way of thinking for investors to realize the CE of solar energy.This study has also demonstrated that the CBA method has a good performance in the decision-making of the optimal site selection for solar power plants. In the scenarios set up in this article, the appropriate ranking for the site alternatives using the CBA method is S2 > S1 > S3. The results show that the CBA method can provide more transparent and objective decision-making than the traditional MCDM methods, making the task easier for the decision-makers. The CBA method can fully reflect the impact of cost on decision-making and allows the experts and stakeholders to make a sagacious choice based on the cost analysis to reduce the risk of the project.The main limitation of this article is that it does not discuss the treatment of PV modules after the end of the lifespan of a solar power plant. Future research will closely link the optimal site selection of solar power plants by considering waste recycling and other relevant factors related to the CE.


The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user’s intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value. The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. In recent years, with the rapid development of computer science and people’s continuous in-depth exploration of brain science, brain-computer interface (BCI) technology has been attracting attention as a new type of human-computer interaction system that directly interacts between brain nerves and peripheral devices. The BCI system collects EEG by placing sensors on the surface of the user’s scalp or inside the skin. The EEG signal is decoded to determine the intention, and then complete a series of operations such as controlling external equipment. Using BCI technology can help patients with movement disorders caused by nerve damage regain the ability to move independently and smoothly interact with the outside world. This technology will significantly improve the quality of life of patients and reduce the burden on families and society. The first international BCI conference held in 1999 clearly defined the brain-computer interface, that is, “BCI is a communication control system for direct communication between the brain and external devices, and does not rely on brain nerves and peripheral muscle tissue” . NowadayMotor Imagery (MI) EEG has the characteristics of flexibility, non-invasiveness, low environmental requirements, and high resolution. Therefore, MI is one of the widely used forms of BCI. The frequency band power of the EEG signal during the motion imaging process will vary with the content of the MI task, which is called event related synchronization/desychronization (ERS/ERD). The generation of ERS/ERD is related to internal or external events. When one side of the human limb exercises or performs motor imagination, the energy of μ rhythm and β rhythm in the sensory motor area on the opposite side of the brain decreases, and the energy of μ rhythm and β rhythm in the ipsilateral motor sensory area increases. This rule makes it possible for ERS/ERD to control external equipment or perform motor imagination intention recognition . Motor IIn 1973, the American Jacques Vidal team put forward the concept of brain-computer interface technology for the first time in a paper, and pointed out that it is an interdisciplinary technology covering various disciplines, including biomedicine, neuroscience, cognitive science, computer science and other fields . HoweverAlthough China’s sports imagination BCI technology started late, it has also made some progress. The BCI team led by Professor Gao Shangkai of Tsinghua University developed a BCI system in 2007 that uses left and right hand and foot motion imaginary EEG signals to control robotic dogs to complete football kicks . In 2010Many experts and scholars have achieved excellent results in the research of brain-computer interface systems and various analysis methods for EEG signals, which has also made a huge promotion for the scientific and effective analysis of EEG signals. Juneja et al. proposed an EEG classification method based on the ELM model, which is characterized by extracting the individual and mutual features of the data set . Tan et (1)The structure and operation process of the MI-BCI system are introduced. The system mainly includes three modules: preprocessing, feature extraction, and classification and recognition. The preprocessing steps in this article include data interception and band-pass filtering. Data interception refers to excluding the preparation and rest phases from the original EEG signal data segment, and only retains the motor imagination phase. Feature extraction includes lead selection, Local Characteristic-scale Decomposition (LCD) feature extraction, and Common Spatial Pattern (CSP) feature extraction. The classification recognition method is the ELM regression model, and the classification is performed by the regression method.(2)A feature fusion method is used in the feature extraction module. The time domain features obtained based on the LCD feature extraction method and the spatial domain features obtained based on the CSP feature extraction method are combined in series to obtain a fusion feature.(3)The classic ELM model is used for classification and recognition. The input weight matrix and hidden neuron bias matrix of ELM’s hidden layer is randomly generated and obey any continuous probability distribution, so only the output weight matrix is required to be solved, and the input weight and hidden layer bias need not be adjusted iteratively. Therefore, the calculation amount and time complexity of the algorithm will be much smaller, and the training speed will be much faster. Recognition based on this model is more suitable for tasks that require high response time.The MI-BCI system has different practical applications in different fields and scenarios, but a complete MI-BCI system is basically composed of five parts as shown in (1)EEG signal acquisition. EEG signal acquisition is mainly responsible for EEG signal acquisition and storage. EEG signal acquisition methods can be divided into two types: non-implantable and implantable. In actual research, non-implantable acquisition methods are used in most cases, that is, the acquisition electrode is directly placed on the corresponding position of the subject’s scalp to record the EEG signal. At the same time, the collected EEG signal is amplified and processed and A/D converted, and the processed result is stored.(2)Pretreatment. The signal-to-noise ratio of the EEG signal is very low, and it is easily affected by external noise and other biological signals. It is very necessary to preprocess the EEG signal before analyzing and processing it, so as to reduce the artifact interference in the signal and improve the signal quality.(3)Feature extraction. Feature extraction is one of the most important links in the MI-BCI system. Its purpose is to learn distinguishing features from the preprocessed EEG data, which is the feature that best reflects the true thinking activity of the brain. The commonly used methods include short-time Fourier transform, wavelet packet transform, common space mode algorithm, etc. .(4)Classification and identification. The classification and recognition of EEG signals is one of the key links that determines the performance of the BCI system. The role of this link is to integrate the extracted features and build a classification model. The classification model can establish a mapping relationship between the signal features and the subjects’ real consciousness activities, so as to realize the feature classification of EEG signals. For EEG signals with different characteristics, commonly used classifiers include Fisher discriminant classifier, support vector machine, neural network algorithm, etc. .(5)Equipment control system. The function realized by the equipment control system is to convert the classification and recognition results of EEG signals into control commands for peripheral equipment. Common peripheral devices include character input systems, smart wheelchairs, robotic arms, etc.The identification process is shown in It is necessary to select the lead before feature extraction. The pre-processed signals are calculated and sorted by the contribution rate, and the four leads with the highest information validity are distinguished from the remaining 18 leads. LCD  is used ijISC(t) refers to the first three layers of ISC components with length N obtained by decomposing each lead. Through the above processing, the time-frequency domain feature vector group V1 =  is obtained. CSP  has been widely used because of its very strong nonlinear approximation ability. The latest research progress on SLFN learning is the Extreme Learning Machine (ELM) algorithm , 2006. EIn the Input Layer, each sample of each training set will have a corresponding weight and offset. There are two ways in ELM: one is to manually enter these weights and offsets, and the other is to automatically generate weights and offsets through ELM toolbox. Random generation is based on the size of the original data and the sigmoid neuron function. For a linear Output Layer, randomly generated weights can better reflect the performance advantages of ELM. ELM is a regression model, but it is also suitable for classification. If the different categories are separate and independent, create a target for each category separately. The target of the class that can be matched is set to 1, and the target of the unmatched class is set to 0. This encoding creates a unit-length vector for each category. This vector is orthogonal to all other classes of vectors. The distance between the target vectors of different categories is the same, so the independence of the categories can be maintained. The prediction category is assigned based on the target with the largest ELM output.g(x) is infinitely differentiable, then, for a given arbitrarily small approximation error, the input layer weights and hidden layer thresholds arbitrarily select the number of hidden nodes N′≤N, where N is the number of training samples. According to the above theory, when the activation function g(x) is infinitely differentiable, all network parameters do not need to be adjusted. The input weight iw and the hidden layer threshold ib can be randomly assigned during training, and they are fixed during the training process. For the selected sample data, the hidden layer output matrix is fixed. Therefore, the parameter training of ELM is transformed into solving the following linear regression problem:Literature  pointed Hβ = T. For the multiple-input single-output data set D = {|nx∈pR,nt∈R,n = 1,2,…,N.}, the following optimization problems can be solved:Here, β can be obtained by solving the least squares solution of the linear system 1,β2,…,βN′]T is the output node, T = T is the hidden layer connection weight vector. The least square solution of Eq. (3) iswhere H+ is the Moore-Penrose generalized inverse of the hidden output matrix H. The optimal solution A has the following important characteristics (1) The smallest training error can be obtained through this solution; (2) The weight vector of the smallest normal form is obtained; (3) The least squares solution of the paradigm is unique, so the algorithm will not produce a local optimal solution.where The data set used in this article is the BCI competition data set provided by Graz University. The description of the experimental data is shown in To verify the effectiveness of the method used in this paper, the selected contrast classifiers include support vector machine SVM , Bayesiaacc represents the classification accuracy rate, and N represents the number of categories. The value range of kappa is . The larger the value, the stronger the consistency and the higher the classification accuracy. During the experiment, the data set was divided into training set and test set. The training set is used to build the classifier model, and the test set is used to evaluate the accuracy of the model for predictive classification of unknown samples. To scientifically test the classification accuracy rate and avoid misleading the experimental results caused by a single classification result, this article adopts fivefold cross validation. Each time, the data set is divided into five parts, and four of them are used as training data to obtain the model completely randomly. The remaining one is classified as test data. The trained classifier model is used to classify the test set. From the classification results, the accuracy rate acci  and the Kappa coefficient Ki  can be obtained. The above process is repeated five times, and the average value of five times is used as the final accuracy of the algorithm. The final calculation formula of the experimental results is as follows, where F is 5.where (1) Data Sets IIbIn order to demonstrate the effectiveness of the method in this paper, this paper compares feature extraction and classifier performance. The experimental results on the Data Sets IIb data set are shown in The experimental results on Data Sets II give two feedback points. (1) The three features are the time domain features based on the LCD feature extraction method, the spatial domain features based on the CSP feature extraction method, and the fusion feature obtained by combining the time domain feature and the spatial domain feature. The classification results obtained based on the above three features show that the classification effect obtained by fusing the features is the best. Different feature extraction methods perform basically the same on the four classification algorithms. For each classifier, the classification effect based on fusion features is the best. This shows that the information of fusion features is richer and more comprehensive, which is more conducive to classification tasks. In most classification algorithms, the classification effect based on the LCD feature extraction method is better than the classification effect based on the CSP feature extraction method. This shows that time domain features have higher information value than spatial domain features. (2) Comparing the four classification algorithms, the ELM algorithm used in this article has the best performance, followed by TSK, third is SVM, and the worst is Bayesian. For other types of classification algorithms, ELM can initialize the input weights and biases randomly to get the corresponding output weights. This algorithm can be faster than traditional learning algorithms under the premise of ensuring learning accuracy. This is the reason why this algorithm is chosen as the classification model in this article.(2) Data Sets IIIaThe classification results on the Data Sets IIIa data set are shown in The data set on Data Sets IIIa is classified into four categories, and the experimental results obtained are somewhat different from the experimental results on Data Sets II. By analyzing the experimental data in A novel smart motor imagery intention human-computer interaction model using extreme learning machine and EEG signals is proposed. Aiming at the problems of low recognition accuracy, large individual differences, and feature redundancy in the current multi-classification problem of motor imagery EEG, this paper deeply studies the signal recognition methods of different limb motor imagery to improve the application of BCI system. The main work of this paper is summarized as follows: (1) A multi-feature fusion extraction method, namely the feature extraction method of LCD+CSP, is used. This method can extract the time-frequency domain and spatial domain features of the data set. The experimental results show that the classification result obtained by a single feature is not as good as the classification result obtained by the fusion feature. (2) The selected ELM algorithm is a mature and widely used classification algorithm with relatively high classification accuracy and fast running speed. ELM can initialize the input weights and biases randomly to get the corresponding output weights. This algorithm can be faster than traditional learning algorithms under the premise of ensuring learning accuracy. The experimental results verify that the ELM algorithm has certain classification advantages. The motion image intention recognition effect based on the method in this paper is close to the champion algorithm of BCI competition. Therefore, the human-computer interaction model designed based on the recognition results of this method is feasible to help patients recover. However, the data set used in this article is relatively simple, and they are all data sets in the BCI competition. In the follow-up, this research plans to use richer data sets for experimental research to verify the universality of the algorithm in this paper. The key to the effectiveness of the ELM model is whether it is necessary to extract features from the data. If feature extraction is meaningless, ELM can come in handy. If it is for the original data set, and feature extraction is very important, the classification effect of ELM is not ideal. In addition, ELM sacrifices too many meaningful patterns in data for speed. Therefore, how to improve ELM to determine the balance between classification accuracy and speed is a future research work.http://www.bbci.de/competition/iv/ and http://www.bbci.de/competition/iii/.Publicly available datasets were analyzed in this study. This data can be found here: YG developed the theoretical framework and model in this work and drafted the manuscript. YG and LH implemented the algorithm and performed experiments and result analysis. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.