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Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology 认领 引用
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作者 Hongyu Sun Yang Xiang Mingdao Yang 《Neural Regeneration Research》 SCIE CAS CSCD 2011年第28期2198-2202,共5页
The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classificati... The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients. 展开更多
关键词 brain-computer interface motor cortex neuronal plasticity rehabilitation stroke neural regeneration
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EEG Foundation Models for Brain-Computer Interfaces:Progress and Future Directions 认领 引用
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作者 DAI Renjie DONG Shenhua +1 位作者 LU Baoliang ZHENG Weilong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第3期783-799,I0047,共17页
Electroencephalography(EEG)foundation models are increasingly used as general-purpose backbones for brain-computer interfaces(BCIs)by leveraging large-scale pretraining and task-specific adaptation.This review summari... Electroencephalography(EEG)foundation models are increasingly used as general-purpose backbones for brain-computer interfaces(BCIs)by leveraging large-scale pretraining and task-specific adaptation.This review summarizes recent progress in EEG foundation models from three perspectives:datasets and task coverage,with emphasis on how generalization goals are operationalized by split protocols and concrete evaluation procedures;model design choices,including input construction and tokenization,masked pretraining objectives,and Transformer backbones for spatiotemporal modeling across heterogeneous channel layouts;and downstream adaptation,comparing linear probing,full fine-tuning,and parameter-efficient tuning,while clarifying the conditions under which each setting is most informative.We emphasize that reported gains are often protocol-dependent,as differences in task scope,preprocessing,training budget,and baseline selection can substantially affect comparability and the extent to which conclusions generalize.Finally,we outline future directions for EEG foundation models in BCI,focusing on standardized evaluation infrastructure,EEG-tailored modeling choices,and deployment-aware adaptation under real-world constraints. 展开更多
关键词 electroencephalography(EEG) foundation model brain-computer interface deep learning
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Wafer-Level Self-Assembly and Interface Passivation Patterning Technology for Nanomaterial-Compatible 3D MEMS Sensing Chips 认领 引用 被引量：1
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作者 Zheng Zhang Yanlin Zhang +4 位作者 Yuanyuan Luo Guoliang Lv Jianglin Yin Pengwei Tan Guotao Duan 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第7期106-121,共16页
Wafer-scale fabrication of high-performance microelectro-mechanical systems(MEMS)bio/chemical sensing chips remains constrained by the absence of reliable methods for integrating high-performance nanomaterials into su... Wafer-scale fabrication of high-performance microelectro-mechanical systems(MEMS)bio/chemical sensing chips remains constrained by the absence of reliable methods for integrating high-performance nanomaterials into suspended MEMS architectures.Here,a wafer-level manufacturing strategy is presented that redefines the MEMS process flow as“film first,cantilever later.”Through kinetically controlled self-assembly,wet-chemically synthesized Pd/SnO2nanospheres are transferred as dense,uniform monolithic films onto 8-inch wafers.An HfO2interface passivation patterning technology resolves long-standing incompatibility between functional sensing films and silicon substrates,enabling precise patterning and reliable integration on suspended MEMS cantilevers.The resulting Pd/SnO2MEMS H2 chips are fabricated onto an 8-inch wafer,demonstrating high sensitivity and consistency.This approach overcomes long-standing wafer-level manufacturing challenges in the formation and patterning of high-performance nanomaterials film,establishing a fully integrated wafer-level process that fundamentally redefines the manufacturing route for tetramethylammonium hydroxide-resistant nanomaterial-based MEMS sensing chips. 展开更多
关键词 Compatible manufacturing Wafer-level self-assembly Interface passivation patterning technology MEMS Sensing chips
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A Personalized Brain-computer Interface Paradigm and Decoding Method for The Objective Evaluation of Auditory Frequency Difference Limen 认领 引用
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作者 LI Sheng-Ye XIAO Xiao-Lin +4 位作者 YU Shi-Hang ZHANG Bei-Bei AN Xing-Wei XU Min-Peng MING Dong 《生物化学与生物物理进展》 SCIE CAS CSCD 北大核心 2026年第7期1927-1941,共15页
Objective The frequency difference limen(FDL)serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system.However,traditional measurement metho... Objective The frequency difference limen(FDL)serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system.However,traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors.Furthermore,existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences.Based on brain-computer interface(BCI)technology,this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state.Methods We designed a personalized rapid serial auditory presentation(RSAP)paradigm customized based on each individual’s precise FDL.A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4000 Hz.This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli,thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state.Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains,this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy,innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network(MS-STAMNet).Specifically,the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals,further deeply integrating multi-scale information through crossbranch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses.Results The comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features,reflecting pre-attentive mismatch detection and subsequent cognitive evaluation,which were prominently distributed over the frontal,central,and temporal regions of the scalp.In the complex time-frequency domain,the extracted neural response characteristics exhibited distinct,statistically significant event-related synchronization within both the low-frequencyδandθfrequency bands,which was simultaneously accompanied by a widespread,prominent event-related desynchronization within the higherαband.A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall(UAR)of(69.67±6.12)%and area under the curve(AUC)of 0.7618±0.07,significantly outperforming the established baseline models such as EEGNet and PLNet.Furthermore,a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis(R2=0.016,P=0.709),indicating that this model can effectively capture the implicit features of subtle frequency deviations,even when they fail to trigger explicit conscious responses.Additionally,attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions.Conclusion This study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation.Concurrently,it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak,single-trial electroencephalogram signals amidst complex background noise.Ultimately,these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications,transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments. 展开更多
关键词 frequency difference discrimination brain-computer interface rapid serial auditory presentation personalization deep learning
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A framework for human brain-computer interfaces with single-neuron recordings 认领 引用
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作者 Chen-Yu Pang Jun-Jie Wang +7 位作者 Ana Sanchez Jimenez Enrique Fernández Serra Santiago Cordisco Gonzalez Chen-Cheng Zhang Qiang-Qiang Liu Ji-Wen Xu Yong Lu Rodrigo Quian Quiroga 《LabMed Discovery》 CAS 2026年第1期33-39,共7页
Objectives Brain-computer interfaces(BCIs)are currently used in clinical studies but mostly rely on population-level signals that limit their precision,facing challenges of interpretability and limited temporal-spatia... Objectives Brain-computer interfaces(BCIs)are currently used in clinical studies but mostly rely on population-level signals that limit their precision,facing challenges of interpretability and limited temporal-spatial specificity.This review describes human single-neuron recordings and new evidence of concept cells from Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,China,and proposes a framework to apply these recordings for closed-loop single-neuron BCIs.Methods We summarize the methodology enabling human single-neuron recordings using Behnke-Fried macro-micro electrodes implanted for monitoring epileptic patients with intracranial recordings.To illustrate feasibility,we present single-unit data from four patients at Ruijin Hospital and describe the procedures for paradigm design,spike detection and sorting,as well as neuronal response identification,and discuss this within the framework of current BCI clinical applications.Results Concept cells can be reliably identified in clinical settings in China using single-neuron recordings.In parallel,current deep-brain BCIs that use local field potential signals have shown therapeutic value in epilepsy,Parkinson’s disease,depression,and memory modulation,but their applications remain limited due to the coarse precision of the signals.By integrating clinical advances with single-neuron recording,we outline two closed-loop strategies:(1)adaptive neural feedback systems that facilitate new studies with human single neuron recordings and particularly with concept cells;and(2)adaptive neuromodulation systems that adjust stimulation parameters on the basis of single-neuron responses to study memory processing.Conclusions Human single-neuron recordings provide a unique opportunity to link deep-brain neuronal activity with high-level cognitive processes.Our findings demonstrate that concept cells can be reliably identified in clinical settings and offer a powerful substrate for next-generation deep-brain BCIs.A closed-loop framework based onsingle-neuron responses may enhance both cognitive research and therapeutic interventions.Achieving clinical translation will require further studies of long-term signal stability,decoding robustness,and scalable integration with existing deep-brain stimulation technologies. 展开更多
关键词 Brain-computer interface(BCI) Single-neuron recording Concept cell Alzheimer’s disease Memory enhancement
Plugged In,Powered Up Once a futuristic concept,brain-computer interfaces are now racing toward the real world and a trillion-yuan market 认领 引用
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作者 Li Shimeng 《China Report ASEAN》 2026年第5期22-25,共4页
Brain-computer interface(BCI)technology,once the stuff of science fiction,is rapidly moving into the real world at an unprecedented pace.Healthcare has emerged as the primary application domain for BCI,where diverse t... Brain-computer interface(BCI)technology,once the stuff of science fiction,is rapidly moving into the real world at an unprecedented pace.Healthcare has emerged as the primary application domain for BCI,where diverse technological approaches are driving a flourishing and highly dynamic landscape of products. 展开更多
关键词 technological approaches brain computer interfaces healthcare dynamic landscape
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AI Large Model-Driven Adaptive Evolution of Brain-Computer Interface Chips:Technical Architecture,Challenges,and Future Directions 认领 引用
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作者 Borui Cui 《Journal of Electronic Research and Application》 2026年第1期209-220,共12页
This paper focuses on how AI large models,such as Transformers and meta-learning can empower brain-computer interface(BCI)chips to achieve dynamic adaptation,thereby overcoming the limitations of traditional fixed dec... This paper focuses on how AI large models,such as Transformers and meta-learning can empower brain-computer interface(BCI)chips to achieve dynamic adaptation,thereby overcoming the limitations of traditional fixed decoding models that struggle to adapt to individual neural plasticity and dynamic changes in brain states.It analyzes pathways to enhance chip generalization and real-time performance across three technical dimensions:hardware architecture,algorithm optimization,and multimodal fusion.The paper also explores core challenges like data privacy and energy-efficiency tradeoffs.Building on this foundation,it proposes a neuromorphic computing design framework for next-generation chips to advance the intelligent and personalized development of BCI in medical rehabilitation and human-computer interaction. 展开更多
关键词 Brain-computer interface Artificial intelligence Chip Large model-driven Meta-learning
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Recent applications of EEG-based brain-computer-interface in the medical field 认领 引用 被引量：30
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作者 Xiu-Yun Liu Wen-Long Wang +39 位作者 Miao Liu Ming-Yi Chen Tânia Pereira Desta Yakob Doda Yu-Feng Ke Shou-Yan Wang Dong Wen Xiao-Guang Tong Wei-Guang Li Yi Yang Xiao-Di Han Yu-Lin Sun Xin Song Cong-Ying Hao Zi-Hua Zhang Xin-Yang Liu Chun-Yang Li Rui Peng Xiao-Xin Song Abi Yasi Mei-Jun Pang Kuo Zhang Run-Nan He Le Wu Shu-Geng Chen Wen-Jin Chen Yan-Gong Chao Cheng-Gong Hu Heng Zhang Min Zhou Kun Wang Peng-Fei Liu Chen Chen Xin-Yi Geng Yun Qin Dong-Rui Gao En-Ming Song Long-Long Cheng Xun Chen Dong Ming 《Military Medical Research》 SCIE CAS CSCD 2025年第8期1283-1322,共40页
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC... Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility. 展开更多
关键词 Brain-computer interfaces(BCIs) Medical applications Rehabilitation Communication Brain monitoring Diagnosis
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Informed consent competency assessment for brain-computer interface clinical research and application in psychiatric disorders:A systematic review 认领 引用 被引量：1
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作者 Jia-Yue Si Zi-Yan Lin +8 位作者 Di-Ga Gan Xin-Yang Zhang Yan-Nan Liu Yu-Xin Hu Yan-Ping Bao Xue-Qin Wang Hong-Qiang Sun Xin Yu Lin Lu 《World Journal of Psychiatry》 SCIE 2025年第8期404-423,共20页
BACKGROUND Brain-computer interface(BCI)technology is rapidly advancing in psychiatry.Informed consent competency(ICC)assessment among psychiatric patients is a pivotal concern in clinical research.AIM To analyze the ... BACKGROUND Brain-computer interface(BCI)technology is rapidly advancing in psychiatry.Informed consent competency(ICC)assessment among psychiatric patients is a pivotal concern in clinical research.AIM To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders.METHODS A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted.A systematic literature search was performed using PubMed,ScienceDirect,and Web of Science.Peer-reviewed articles and full-text studies were included in the analysis.There were no date restrictions,and all studies published up to February 27,2025,were included.RESULTS A total of 103 studies were selected for this review.Fifty-eight studies included ICC factors,and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders.Executive function impairment is widely recognized as the most significant factor impacting ICC,and processing speed deficits are observed in schizophrenia,mood disorders,and Alzheimer’s disease.Memory dysfunction,particularly episodic and working memory,contributes to compromised ICC.Five core ethical issues in BCI research should be addressed:BCI specificity,vulnerability,autonomy,dynamic ICC,comprehensiveness,and uncertainty.CONCLUSION A Five-Dimensional evaluative framework,including clinical,ethical,sociocultural,legal,and procedural dimensions,is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders. 展开更多
关键词 Informed consent competency Brain-computer interface Psychiatric disorders Decision-making capacity MacArthur Competence Assessment Tool
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Frequency optimization for electrodes in implantable brain-computer interfaces 认领 引用 被引量：1
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作者 CHEN Han LIU Xiangyu +2 位作者 CHENG Jiajun QIN Jiangfan ZHANG Xueli 《Journal of Southeast University(English Edition)》 EI CAS 2025年第3期366-374,共9页
Fully implanted brain-computer interfaces(BCIs)are preferred as they eliminate signal degradation caused by interference and absorption in external tissues,a common issue in non-fully implanted systems.To optimize the... Fully implanted brain-computer interfaces(BCIs)are preferred as they eliminate signal degradation caused by interference and absorption in external tissues,a common issue in non-fully implanted systems.To optimize the design of electroencephalography electrodes in fully implanted BCI systems,this study investigates the penetration and absorption characteristics of microwave signals in human brain tissue at different frequencies.Electromagnetic simulations are used to analyze the power density distribution and specific absorption rate(SAR)of signals at various frequen-cies.The results indicate that lower-frequency signals offer advantages in terms of power density and attenuation coeffi-cients.However,SAR-normalized analysis,which considers both power density and electromagnetic radiation hazards,shows that higher-frequency signals perform better at superficial to intermediate depths.Specifically,at a depth of 2 mm beneath the cortex,the power density of a 6.5 GHz signal is 247.83%higher than that of a 0.4 GHz signal.At a depth of 5 mm,the power density of a 3.5 GHz signal exceeds that of a 0.4 GHz signal by 224.16%.The findings suggest that 6.5 GHz is optimal for electrodes at a depth of 2 mm,3.5 GHz for 5 mm,2.45 GHz for depths of 15-20 mm,and 1.8 GHz for 25 mm. 展开更多
关键词 brain-computer interfaces electromagnetic simulation electroencephalography electrodes power den-sity specific absorption rate
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Application of Brain-Computer Interface in the Treatment of Mental Illness 认领 引用
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作者 Yuda Song Caiyi Yang Peichen Yu 《Journal of Clinical and Nursing Research》 2025年第9期218-224,共7页
Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing n... Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing new possibilities for the treatment and rehabilitation of mental illnesses.This paper reviews the advantages,existing risks,and challenges of BCI technology in mental health treatment,and prospects the future development of research on BCI and mental health. 展开更多
关键词 Brain-computer interface Mental health Psychotherapy
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A non-invasive algorithm for removing artifacts of eye movement,environment and contact sweat in frontal brain-computer interfaces based on BIAS multi-bias modeling 认领 引用
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作者 Quanhong Che Chongliang Zhong +2 位作者 Na Li Haozhang Tan Zhong Wang 《Science International Innovative Medicine》 2025年第6期54-62,共9页
Aiming at low SNR and feature distortion of valid neural signals caused by eye movement,electromagnetic interference and channel noise in frontal single/multi-channel BCI EEG tests,a BIAS-core anchor artifact removal ... Aiming at low SNR and feature distortion of valid neural signals caused by eye movement,electromagnetic interference and channel noise in frontal single/multi-channel BCI EEG tests,a BIAS-core anchor artifact removal algorithm is proposed for low-channel,low-computing wearable scenarios.It defines EOG-BIAS,ENV-BIAS,CH-BIAS,builds a"pre-calibration-real-time processing-posterior compensation"lightweight architecture,and decouples artifacts via"bias modeling-separation-compensation".With unsupervised learning and individual adaptability,it fits unshielded real-time environments.Tests show its EOG/ENV-BIAS removal rates≥95%/≥99%,neural signal retention≥90%,single-frame delay<10ms,SNR improvement≥15dB(stable≥10dB),effectively ensuring frontal BCI signal quality and supporting low-resource frontal BCI signal preprocessing. 展开更多
关键词 Non-invasive brain-computer interface Frontal EEG Artifact removal BIAS algorithm
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Trends and advanced techniques in high-speed simultaneous bi-directional transceivers for die-to-die interfaces 认领 引用
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作者 Yufeng Ge Ziqi Xue +5 位作者 Xuanyu Li Aoxuan Wen Hongzhi Wu Pingyi Cai Xuxu Cheng Quan Pan 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期18-23,共6页
The rapid proliferation of AI and high-performance computing(HPC)applications is driving chip-to-chip and die-todie(D2D)interfaces toward substantially higher bandwidth density.These high-speed interface technologies ... The rapid proliferation of AI and high-performance computing(HPC)applications is driving chip-to-chip and die-todie(D2D)interfaces toward substantially higher bandwidth density.These high-speed interface technologies are essential for chiplet architectures,high-bandwidth memory(HBM),and heterogeneous integrated systems.Single-ended simultaneous bidirectional(SBD)technology doubles system throughput by enabling concurrent transmission and reception over a single physical channel. 展开更多
关键词 high performance computing interface technologies heterogeneous integrated systemssingle ended chiplet architectureshigh bandwidth AI die die interfaces chip chip concurrent transmission reception
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Visual Fixation Assessment in Patients with Disorders of Consciousness Based on Brain-Computer Interface 认领 引用 被引量：15
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作者 Jun Xiao Jiahui Pan +7 位作者 Yanbin He Qiuyou Xie Tianyou Yu Haiyun Huang Wei Lv Jiechun Zhang Ronghao Yu Yuanqing Li 《Neuroscience Bulletin》 SCIE CAS CSCD 2018年第4期679-690,共12页
Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised （CRS-R）. Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in ... Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised （CRS-R）. Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness （DOCs）. Brain- computer interface （BCI） can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expres- sion. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R. 展开更多
关键词 Visual fixation Brain-computer interface Disorder of consciousness Coma recovery scale-revised Electroencephalography
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Transfer Learning Algorithm Design for Feature Transfer Problem in Motor Imagery Brain-computer Interface 认领 引用 被引量：3
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作者 Yu Zhang Huaqing Li +3 位作者 Heng Dong Zheng Dai Xing Chen Zhuoming Li 《China Communications》 SCIE CSCD 2022年第2期39-46,共8页
The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal... The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of MI-BCI.In this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing samples.The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification accuracy.Moreover,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI. 展开更多
关键词 brain-computer interface motor imagery feature transfer transfer learning domain adaptation
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Material flow control technology of ironmaking and steelmaking interface 认领 引用 被引量：7
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作者 黄帮福 田乃媛 +1 位作者 施哲 丁跃华 《Journal of Central South University》 SCIE EI CAS 2014年第9期3559-3567,共9页
In order to achieve higher efficient cohesion match of procedure and equipment between ironmaking and steelmaking interface, the theory of multi-dimensional material flow control was applied to analyze torpedo ladle-i... In order to achieve higher efficient cohesion match of procedure and equipment between ironmaking and steelmaking interface, the theory of multi-dimensional material flow control was applied to analyze torpedo ladle-iron ladle transportation process between blast furnace and basic oxygen furnace. Moreover, basic parameters of material flow were analyzed and optimized, such as time, temperature and material quantity. Based on operating principles of material flow, control methods were optimized, such as product organization mode, scheduling discipline and scheduling plan of hot metal ladle. Finally, the material flow control technology of ironmaking and steelmaking interface was integrated. Satisfactory effects are obtained after applying the technology in practice. The total turnover number of torpedo ladle decreases from 20 to 18, the hot metal temperature of 1# BF torpedo ladle decreases from 36 °C to 19.5 °C, the hot metal temperature of 2# BF torpedo ladle decreases from 36.6 °C to 19.8 °C, the temperature drop of desulfurization hot metal decreases by 4 °C, and the temperature drop of non-desulfurization hot metal decreases by 2.8 °C. Furthermore, the ironmaking and steelmaking interface system will realize high-efficiency control by using this control technology. 展开更多
关键词 ironmaking and steelmaking interface torpedo ladle hot metal ladle material flow control control technology
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Electric Wheelchair Control System Using Brain-Computer Interface Based on Alpha-Wave Blocking 认领 引用 被引量：3
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作者 明东 付兰 +8 位作者 陈龙 汤佳贝 綦宏志 赵欣 周鹏 张力新 焦学军 王春慧 万柏坤 《Transactions of Tianjin University》 EI CAS 2014年第5期358-363,共6页
A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control... A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram(EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject's triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min. 展开更多
关键词 electric wheelchair alpha-wave blocking brain-computer interface （BCI） success control rate
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Non-invasive EEG-based brain-computer interfaces in patients with disorders of consciousness 认领 引用 被引量：6
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作者 Emilia Mikoajewska Dariusz Mikoajewski 《Journal of Medical Colleges of PLA(China)》 2014年第2期109-114,共6页
Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for re... Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions. 展开更多
关键词 neurological disorders disorders of consciousness brain-computer interfaces EEG-based BCIs
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Individualization of Data-Segment-Related Parameters for Improvement of EEG Signal Classification in Brain-Computer Interface 认领 引用 被引量：1
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作者 曹红宝 BESIO Walter G +1 位作者 JONES Steven 周鹏 《Transactions of Tianjin University》 EI CAS 2010年第3期235-238,共4页
In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in... In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI. 展开更多
关键词 data segment parameter selection EEG classification brain-computer interface （BCI）
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Remote Navigation of Turtle by Controlling Instinct Behavior via Human Brain-computer Interface 认领 引用 被引量：9
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作者 Cheol-Hu Kim Bongjae Choi +3 位作者 Dae-Gun Kim Serin Lee Sungho Jo Phill-Seung Lee 《Journal of Bionic Engineering》 SCIE EI CSCD 2016年第3期491-503,共13页
Brain-Computer Interface （BCI） techniques have advanced to a level where it is now eliminating the need lor hand-based activation. This paper presents a novel attempt to remotely control an animal＇s behavior by hum... Brain-Computer Interface （BCI） techniques have advanced to a level where it is now eliminating the need lor hand-based activation. This paper presents a novel attempt to remotely control an animal＇s behavior by human BCI using a hybrid of Event Related Desynchronization （ERD） and Steady-State Visually Evoked Potential （SSVEP） BCI protocols. The turtle was chosen as the target animal, and we developed a head-mounted display, wireless communication, and a specially designed stimulation device for the turtle. These devices could evoke the turtle＇s instinctive escape behavior to guide its moving path, and turtles were remotely controlled in both indoor and outdoor environments. The system architecture and design were presented. To demon- strate the feasibility of the system, experimental tests were performed under various conditions. Our system could act as a framework for future human-animal interaction systems. 展开更多
关键词 brain-computer interface turtle （Trachemys scripta elegans） remote navigation instinct behaviour escape be- havior
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