检索结果-内蒙古大学图书馆

International Joint Conference on neural Networks

作者： Sim Kuan Goh Hussein A. Abbass Kay Chen Tan Abdullah Al-Mamun Cuntai Guan Chuan Chu Wang Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 117583 Singapore School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Brain-Computer Interface Laboratory Institute for Infocomm Research 1 Fusionopolis Way 138732 Singapore

ISBN: (纸本)9781509006212

neural information recorded from electroencephalogram (EEG) provides new possibilities for diagnosis of brain abnormalities, cognitive monitoring, etc. However, many artifacts, such as eye blink and muscle movements, impact and contaminate EEG data. While traditional techniques proposed for artifact removal identified artifact on two-way data, (spatial × temporal), multidimensional nature of EEG data (spatial × temporal × spectral × condition × trial) is overlooked. In this work, we investigate the use of multiway analysis/tensor factorization on the extended EEG tensor (spatial × temporal × spectral), which is constructed from continuous wavelet transform, using Block Term Decomposition (BTD) of rank-(L_r, L_r, 1) for artifact removal. Eight different carefully designed experiments to study artifact typically produced by voluntarily, and sometimes involuntarily, behaviors using a subject were performed and analyzed. After the BTD decomposition, artifacted components are automatically identified removed using spatial and temporal features. The reconstructed signal from proposed method suppresses artifact while retains the signal texture of eight types of artifact investigated.

关键词： Electroencephalography Tensile stress Muscles Wavelet transforms Mouth Electrodes

来源：评论

学校读者我要写书评

暂无评论

Global and risk-group stratified well-being and mental health during the COVID-19 pandemic in adults: Results from the international COH-FIT Study

引用

Psychiatry Research 2024年 342卷 115972-115972页

作者： Solmi, Marco Thompson, Trevor Estradé, Andrés Agorastos, Agorastos Radua, Joaquim Cortese, Samuele Dragioti, Elena Vancampfort, Davy Thygesen, Lau Caspar Aschauer, Harald Schlögelhofer, Monika Aschauer, Elena Schneeberger, Andres Huber, Christian G. Hasler, Gregor Conus, Philippe Cuénod, Kim Q. Do von Känel, Roland Arrondo, Gonzalo Fusar-Poli, Paolo Gorwood, Philip Llorca, Pierre-Michel Krebs, Marie-Odile Scanferla, Elisabetta Kishimoto, Taishiro Rabbani, Golam Skonieczna-Żydecka, Karolina Brambilla, Paolo Favaro, Angela Takamiya, Akihiro Zoccante, Leonardo Colizzi, Marco Bourgin, Julie Kamiński, Karol Moghadasin, Maryam Seedat, Soraya Matthews, Evan Wells, John Vassilopoulou, Emilia Gadelha, Ary Su, Kuan-Pin Kwon, Jun Soo Kim, Minah Lee, Tae Young Papsuev, Oleg Manková, Denisa Boscutti, Andrea Gerunda, Cristiano Saccon, Diego Righi, Elena Monaco, Francesco Croatto, Giovanni Cereda, Guido Demurtas, Jacopo Brondino, Natascia Veronese, Nicola Enrico, Paolo Politi, Pierluigi Ciappolino, Valentina Pfennig, Andrea Bechdolf, Andreas Meyer-Lindenberg, Andreas Kahl, Kai G. Domschke, Katharina Bauer, Michael Koutsouleris, Nikolaos Winter, Sibylle Borgwardt, Stefan Bitter, Istvan Balazs, Judit Czobor, Pál Unoka, Zsolt Mavridis, Dimitris Tsamakis, Konstantinos Bozikas, Vasilios P. Tunvirachaisakul, Chavit Maes, Michael Rungnirundorn, Teerayuth Supasitthumrong, Thitiporn Haque, Ariful Brunoni, Andre R. Costardi, Carlos Gustavo Schuch, Felipe Barreto Polanczyk, Guilherme Luiz, Jhoanne Merlyn Fonseca, Lais Aparicio, Luana V. Valvassori, Samira S. Nordentoft, Merete Vendsborg, Per Hoffmann, Sofie Have Sehli, Jihed Sartorius, Norman Heuss, Sabina Guinart, Daniel Hamilton, Jane Kane, John Rubio, Jose Sand, Michael Koyanagi, Ai Solanes, Aleix Andreu-Bernabeu, Alvaro Cáceres, Antonia San José Arango, Celso Díaz-Caneja, Covadonga M. Hidalgo-Mazzei, Diego Vieta, Eduard Gonzalez-Peñas, Javier Fortea, Lydia Parellada, Mara Fullana, Miquel A. Verdolini, Norma Andrlíková, Eva Janků, Karolina Millan, Mark J. Honciuc, Mihaela Moniuszko-Mal University of Ottawa Department of Psychiatry ON Canada The Ottawa Hospital Department of Mental Health ON Canada Ottawa Hospital Research Institute (OHRI) Neurosciences Program Ottawa Ontario Canada Early Psychosis: Interventions and Clinical-detection (EPIC) Lab Department of Psychosis Studies Institute of Psychiatry Psychology & Neuroscience King's College London London United Kingdom Centre for Innovation in Mental Health-Developmental Lab School of Psychology University of Southampton and NHS Trust Southampton United Kingdom Charité Universitätsmedizin Berlin Department of Child and Adolescent Psychiatry Berlin Germany Centre for Chronic Illness and Ageing University of Greenwich London United Kingdom University of Southampton Centre for Innovation in Mental Health Southampton United Kingdom Universidad Católica Department of Psychology Montevideo Uruguay Aristotle University of Thessaloniki II. Dept. of Psychiatry Division of Neurosciences Medical School Faculty of Health Sciences Greece Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Imaging of Mood- and Anxiety-Related Disorders (IMARD) University of Barcelona CIBERSAM Instituto de Salud Carlos III Barcelona Spain Karolinska Institutet Centre for Psychiatric Research and Education Department of Clinical Neuroscience Stockholm Sweden Linköping University Pain and Rehabilitation Centre and Department of Health Medicine and Caring Sciences Linköping Sweden University of Ioannina Research Laboratory Psychology of Patients Families & Health Professionals Department of Nursing School of Health Sciences Ioannina Greece Katholieke Universiteit Leuven (KU Leuven) Department of Rehabilitation Sciences Leuven Belgium National Institute of Public Health University of Southern Denmark Denmark BioPsyC - Biopsychosocial Corporation Non-profit association for Research Funding Ltd. Vienna Austria University of California San Diego California United States University of Basel Universitä

International studies measuring wellbeing/multidimensional mental health before/ during the COVID-19 pandemic, including representative samples for >2 years, identifying risk groups and coping strategies are lacking. COH-FIT is an online, international, anonymous survey measuring changes in well-being (WHO-5) and a composite psychopathology P-score, and their associations with COVID-19 deaths/restrictions, 12 a-priori defined risk individual/cumulative factors, and coping strategies during COVID-19 pandemic (26/04/2020-26/06/2022) in 30 languages (representative, weighted non-representative, adults). T-test, χ², penalized cubic splines, linear regression, correlation analyses were conducted. Analyzing 121,066/142,364 initiated surveys, WHO-5/P-score worsened intra-pandemic by 11.1±21.1/13.2±17.9 points (effect size d=0.50/0.60) (comparable results in representative/weighted non-probability samples). Persons with WHO-5 scores indicative of depression screening (<50, 13% to 32%) and major depression (<29, 3% to 12%) significantly increased. WHO-5 worsened from those with mental disorders, female sex, COVID-19-related loss, low-income country location, physical disorders, healthcare worker occupations, large city location, COVID-19 infection, unemployment, first-generation immigration, to age=18-29 with a cumulative effect. Similar findings emerged for P-score. Changes were significantly but minimally related to COVID-19 deaths, returning to near-pre-pandemic values after >2 years. The most subjectively effective coping strategies were exercise and walking, internet use, social contacts. Identified risk groups, coping strategies and outcome trajectories can inform global public health strategies. © 2024 The Authors

关键词： Covid-19 Mental health P-factor Pandemic Psychiatry Psychopathology Survey Well-being WHO-5

来源：评论

学校读者我要写书评

暂无评论

Applications of brain–Machine interface Systems in Stroke Recovery and Rehabilitation

引用

Current Physical Medicine and Rehabilitation Reports 2014年第2期2卷 93-105页

作者： Venkatakrishnan, Anusha Francisco, Gerard E. L. Contreras-Vidal, Jose Laboratory for Non-invasive Brain–Machine Interface Systems Department of Electrical and Computer Engineering University of Houston Houston TX United States Department of Physical Medicine and Rehabilitation University of Texas Health Science Center Houston TX United States NeuroRecovery Research Center TIRR Memorial Hermann Houston Houston TX United States

Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain–machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neurorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation. © 2014, Springer Science + Business Media New York.

关键词： brain–machine interfaces Clinical trials Functional recovery Neuroplasticity Neurorehabilitation Robotic exoskeletons Robotic-assisted rehabilitation Stroke

来源：评论

学校读者我要写书评

暂无评论

Decoding of Intentional Actions from Scalp Electroencephalography (EEG) in Freely-behaving Infants

Decoding of Intentional Actions from Scalp Electroencephalog...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Zachery R. Hernandez Jesus Cruz-Garza Teresa Tse Jose L. Contreras-Vidal Non-Invasive Brain-Machine Interface Systems Laboratory at the Department of Electrical and Computer Engineering University of Houston (UH) Center for Robotics and Intelligent Systems Tecnologico de Monterrey

ISBN: (纸本)9781424479276

The mirror neuron system (MNS) in humans is thought to enable an individual's understanding of the meaning of actions performed by others and the potential imitation and learning of those actions. In humans, electroencephalographic (EEG) changes in sensorimotor a-band at central electrodes, which desynchronizes both during execution and observation of goal-directed actions (i.e., μ suppression), have been considered an analog to MNS function. However, methodological and developmental issues, as well as the nature of generalized μ suppression to imagined, observed, and performed actions, have yet to provide a mechanistic relationship between EEG μ-rhythm and MNS function, and the extent to which EEG can be used to infer intent during MNS tasks remains unknown. In this study we present a novel methodology using active EEG and inertial sensors to record brain activity and behavioral actions from freely-behaving infants during exploration, imitation, attentive rest, pointing, reaching and grasping, and interaction with an actor. We used δ-band (1-4Hz) EEG as input to a dimensionality reduction algorithm (locality-preserving Fisher's discriminant analysis, LFDA) followed by a neural classifier (Gaussian mixture models, GMMs) to decode the each MNS task performed by freely-behaving 6-24 month old infants during interaction with an adult actor. Here, we present results from a 20-month male infant to illustrate our approach and show the feasibility of EEG-based classification of freely occurring MNS behaviors displayed by an infant. These results, which provide an alternative to the μ-rhythm theory of MNS function, indicate the informative nature of EEG in relation to intentionality (goal) for MNS tasks which may support action-understanding and thus bear implications for advancing the understanding of MNS function.

关键词： Electroencephalography Scalp Acceleration Training Coherence Electrodes Testing

来源：评论

学校读者我要写书评

暂无评论

brain-computer interface for stroke rehabilitation with clinical studies

Brain-computer interface for stroke rehabilitation with clin...

引用

2013 International Winter Workshop on brain-computer interface, BCI 2013

作者： Guan, Cuntai Brain-Computer Interface Laboratory Institute for Infocomm Research ASTAR Singapore Singapore

ISBN: (纸本)9781467359733

Stroke is the leading cause of severe disabilities in the developed world. Each year, there are around 15 million new stroke cases worldwide. About 30% of stroke survivors need various forms of rehabilitation. Among these, upper limb weakness and loss of hand function are among the most devastating types of disabilities. Despite optimal acute medical treatment and modern rehabilitation, 45% of the patients do not achieve complete recovery of their bodily functions. In addition, 85% to 90% of stroke survivors with upper limb impairment do not regain full functional use of their upper extremities. Limitations in current physiotherapy and occupational therapy techniques include: (i) difficulties in rehabilitation for the severely paralyzed arm and hand which are often treated with passive modalities, (ii) difficulties in achieving intensive rehabilitation and high repetitions in those with moderate to severe upper extremity paralysis, (iii) problems in motivating and sustaining patient interest in repetitive exercises, (iv) therapy is often perceived to be boring due to lack of immediate biofeedback. © 2013 IEEE.

关键词： Biofeedback

来源：评论

学校读者我要写书评

暂无评论

Modular organization of reaching and grasping movements investigated using EEG microstates

Modular organization of reaching and grasping movements inve...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： J. Minguillon E. Pirondini M. Coscia R. Leeb J.d.R. Millán D. Van De Ville S. Micera Translational Neural Engineering Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland Instituto di Neuroscienze del Consiglio Nazionale delle Ricerche (CNR) Pisa Italy Chair of Non-Invasive Brain-Machine Interface Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Radiology and Medical Informatics University of Geneva Lausanne Switzerland Biorobotics Institute Scuola Superiore Sant'Anna Pisa Italy

How movements are generated and controlled by the central nervous system (CNS) is still not well understood. In this work, we tested the hypothesis of a modular organization of the brain activity during the execution of voluntary movements. In particular, we extracted meta-stable topographies as a measure for global brain state, so-called microstates, from electroencephalography (EEG) data during pure planar reaching movements as well as reaching and grasping of different objects, and we compared them with those extracted during resting-state. The results showed the emergence of specific EEG microstates related to movement execution. Our results provide evidence about the benefits of EEG microstate analysis for motor control studies and their importance to better understand brain reorganization in neurological pathologies.

关键词： Electroencephalography Grasping brain Fingers Organizations Timing Educational institutions

来源：评论

学校读者我要写书评

暂无评论

brain-computer interface for stroke rehabilitation with clinical studies

Brain-computer interface for stroke rehabilitation with clin...

引用

International Winter Workshop on brain-computer interface (BCI)

作者： Cuntai Guan Brain-Computer Interface Laboratory Institute for Infocomm Research Agency for Science Technology and Research Singapore

Summary form only given. Stroke is the leading cause of severe disabilities in the developed world. Each year, there are around 15 million new stroke cases worldwide. About 30% of stroke survivors need various forms of rehabilitation. Among these, upper limb weakness and loss of hand function are among the most devastating types of disabilities. Despite optimal acute medical treatment and modern rehabilitation, 45% of the patients do not achieve complete recovery of their bodily functions. In addition, 85% to 90% of stroke survivors with upper limb impairment do not regain full functional use of their upper extremities. Limitations in current physiotherapy and occupational therapy techniques include: (i) difficulties in rehabilitation for the severely paralyzed arm and hand which are often treated with passive modalities, (ii) difficulties in achieving intensive rehabilitation and high repetitions in those with moderate to severe upper extremity paralysis, (iii) problems in motivating and sustaining patient interest in repetitive exercises, (iv) therapy is often perceived to be boring due to lack of immediate biofeedback.

关键词： brain-computer interfaces DC motors Medical treatment Robots Electroencephalography Conferences Motor drives

来源：评论

学校读者我要写书评

暂无评论

Determination of trajectories using non-invasive bci techniques in 3d environments

Determination of trajectories using non-invasive bci techniq...

引用

16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2013

作者： García-Egea, Teodoro Díaz-Hernández, Carlos Alberto López-Coronado, Juan Contreras-Vidal, José L. Department of Automation and Systems Engineering Campus Muralla del Mar Polytechnical University of Cartagena Cartagena Spain Department of Polytechnic Sciences Polytechnic Faculty UCAM Catholic University San-Anthony of Murcia Spain Fischell Department of Bioengineering University of Maryland College Park MD United States Department of Kinesiology University of Maryland College Park MD United States Department of Electrical and Computer Engineering Laboratory for Non-invasive Brain-Machine Interface Systems University of Houston Houston TX 77204 United States

ISBN: (纸本)9789814525527

The feasibility of continuous decoding of self-initiated, self-selected hand movements to three-dimensional (3D) spatial targets from scalp electroencephalography (EEG) using linear decoders has been recently demonstrated. In this paper, we show that it is possible to train linear classifiers to decode hand movement direction to eight 3D spatial targets, in both planning and movement windows, using only the fluctuations in the amplitude of smoothed low-frequency signals from high-density scalp EEG. Taken together these results support the design of brain-computer interfaces (BCI) based on non-invasive scalp EEG signals and suggest that the current perception of the limits of EEG as a source signal for BCI applications merits further examination. Copyright © 2013 by World Scientific Publishing Co. Pte. Ltd.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Developmental study of neuroblastoma-associated gene mutations

引用

Autonomic Neuroscience 2013年第1期177卷 26-26页

作者： Hideki Enomoto Mayumi Nagashimada Sachie Ohno Division of Neural Differentiation and Regeneration Department of Physiology and Cell Biology Kobe University Graduate School of Medicine Kobe Japan Laboratory for Neuronal Differentiation and Regeneration RIKEN Center for Developmental Biology Kobe Japan Laboratory for Cell Metabolism and Nutrition Brain/Liver Interface Medicine Research Center Kanazawa University Kanazawa Japan Department of Pediatric Surgery Chiba University Graduate School of Medicine Chiba Japan

来源：评论

学校读者我要写书评

暂无评论

Real-time Prediction of Fast and Slow Delivery of Mental Commands in a Motor Imagery BCI: An Entropy-based Approach

Real-time Prediction of Fast and Slow Delivery of Mental Com...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Sareh Saeedi Ricardo Chavarriaga Jose del R. Millan Michael C. Gastpar Chair on Non-Invasive Brain-Machine Interface (CNBI) Center for Neuroprosthetics School of Engineering EPFL Laboratory for Information in Networked Systems School of Computer and Communication Sciences EPFL

ISBN: (纸本)9781467317139

Providing adaptive shared control for brain-computer interfaces (BCIs) can result in better performance while reducing the user's mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This study aims at real-time differentiation between fast and slow trials in a motor imagery BCI. In our experiments, we refer to trials shorter than the median of trial lengths as "fast" trials and to those longer than the median as "slow" trials. We propose a classifier for real-time distinction between fast and slow trials based on estimates of the entropy rates for the first 2-3 s of the electroencephalogram (EEG). Results suggest that it can be predicted whether a trial is slow or fast well before a cutoff time. This is important for adaptive shared control especially because 55% to 75% of trials (for the five subjects in this study) are longer than that cutoff time.

关键词： brain-computer interface (BCI) Shared Control EEG Entropy Fasting experimentation Trials commands Imagery (Psychotherapy) images Median Motor trial Classifiers Obstetric Delivery Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：