检索结果-内蒙古大学图书馆

2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Gonzalez-Cely, A.X. Dantas, André Felipe Oliveira De Azevedo Santo, Caroline Cunha Do Espirito Bastos-Filho, Teodiano F. Delisle-Rodriguez, Denis Federal University of Espirito Santo Robotics and Assistive Technology Laboratory Vitoria Brazil Santos Dumont Institute Postgraduate Program In Neuroengineering Macaiba Brazil

ISBN: (纸本)9798350361025

Providing alternatives for lower limb moment control in post-stroke or post-Spinal Cord Injury (SCI) patients who have suffered a severe neural impairment many years ago can further improve the effect of rehabilitation. This work proposes a multitask system for dorsiflexion of the right, left, and both feet through Electroencephalography and Functional Electrical Stimulation (FES). A Butterworth bandpass filter between 8 and 30 Hz is implemented, and subsequently, three strategies are developed by first considering the power spectral density features in mu, low-beta, and high-beta bands responsible for motor task execution. Moreover, the second strategy uses Common Spatial Patterns filters, and the third strategy uses Riemannian Geometry (RG) to extract features. Four distinct classifiers were explored: k-nearest neighbors, support vector machine, Linear Discriminant Analysis (LDA), and decision tree. Based on Accuracy (ACC), True Positive Rate (TPR), and False Positive Rate (FPR) were evaluated the classifiers and the strategies implemented by achieving an ACC of 0.79, a TPR of 0.89 and a FPR of 0.05 employing RG + LDA. The implemented methodology allows the feasibility of a Human-Machine Interface (HMI) which implements a lower-limb MI task recognition system to be used in rehabilitation. Future work will implement the recognition system in real-time to users move the desired limb by activating a FES that generates contraction. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A novel methodology based on static visual stimuli and kinesthetic motor imagery for upper limb neurorehabilitation

引用

Research on Biomedical Engineering 2024年第3-4期40卷 687-700页

作者： Bastos-Filho, T.F. Villa-Parra, A.C. Guerrero-Méndez, C.D. González-Cely, A.X. Blanco-Díaz, C.F. Delisle-Rodríguez, D. Igasaki, T. Vitoria Brazil Biomedical Engineering Research Group Salesian Polytechnic University Cuenca Ecuador Postgraduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neurosciences Santos Dumont Institute Macaiba Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

Purpose: This work proposes a protocol to be used in a Brain-Computer Interface (BCI) for hand and finger movement rehabilitation aimed at post-stroke patients. This strategy is based on Electroencephalography (EEG) and uses concepts of both Static Visual Stimuli (SVS) of the own subject’s hand (OwnSVS) and Kinesthetic Motor Imagery (KMI) to improve the motor task recognition of the same limb. Methods: The protocol proposed here takes into account several aspects, such as physical rehabilitation strategies, Active Hand Orthosis (AHO) — represented by a robotic glove —, and the acquisition of the EEG signal over the brain motor area. Power Spectral Density (PSD) and Riemannian Geometry (RG) are used for feature extraction in this work, considering Mu (μ, 8–12 Hz), Low-Beta (Low-β, 13–17 Hz) and High-Beta (High-β, 18–24 Hz) frequency bands. Moreover, a feature selection stage using Pair-Wise Feature Proximity (PWFP) is also used before input to a Machine Learning (ML) classifier. Here, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Decision Tree (DT) are compared. Results: As results, evaluated in 10 healthy subjects, features using PSD-PWFP and kNN achieved a maximum ACC of 89.81% (with AHO movements), improving by 3% the standard protocol (without AHO movements). Conclusions: The findings of this work indicate that AHO movement assistance can improve the recognition of hand opening and closing MI tasks, which can be implemented in the design of robotic BCI controllers for hand and finger movement neurorehabilitation in stroke patients. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2024.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Towards a Low-Cost Electroencephalogram for a Brain-Machine Interface-Based Functional Electrical Stimulation 9th

Towards a Low-Cost Electroencephalogram for a Brain-Machi...

引用

9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Dunga, Douglas Viana, Bruna Rodrigues, Vitor Dantas, André Rodriguez, Denis Postgraduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neurosciences Santos Dumont Institute Av. Alberto Santos Dumont 1560 - Zona Rural. Macaiba/RN59280-000 Brazil

ISBN: (纸本)9783031494062

This work presents the development of a single electroencephalogram (EEG) channel, which will be integrated in a brain-machine interface based on functional electrical stimulation (FES). As a first stage, the EEG channel was designed and tested on protoboard, conducting three experiments, such as eyes blinking acquisition on the prefontal (FP1 location), eyes opening and closing acquisition on the occipital region (O1 location), and Steady State Visually Evoked Potential (SSVEP) application by analyzing the EEG signal over O1 for visual stimuli at 8, 10, and 12 Hz. The results in time and frequency domain using the Welch periodogram show a good performance of our designed EEG channel. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Multitask Lower Limb Classification System for Dorsiflexion Rehabilitation by Using Electroencephalography and Functional Electrical Stimulation

Multitask Lower Limb Classification System for Dorsiflexion ...

引用

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Gonzalez-Cely A. X. André Felipe Oliveira De Azevedo Dantas Caroline Cunha Do Espirito Santo Teodiano F. Bastos-Filho Denis Delisle-Rodriguez Robotics and Assistive Technology Laboratory Federal University of Espirito Santo Vitoria Brazil Postgraduate Program in Neuroengineering Santos Dumont Institute Macaiba Brazil

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

关键词： Support vector machines Neuroplasticity Filters Muscles Feature extraction Iron Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Review on new approaches of neurorehabilitation therapies for patients with neurological disorders using pedaling devices

引用

Neurologia Argentina 2024年第1期16卷 31-43页

作者： Lobaina Delgado, Alfredo Delisle-Rodriguez, Denis Ferreira da Rocha, Adson Salvador Figueroa, Elizabeth López-Delis, Alberto Departamento de Biomédica Facultad de Ingeniería en Telecomunicaciones Informática y Biomédica Universidad de Oriente Santiago de Cuba Cuba Postgraduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neurosciences Santos Dumont Institute Natal Brazil Departamento de Engenharia Eléctrica Universidade de Brasília Brasilia Brazil Centro de Biofísica Médica Universidad de Oriente Santiago de Cuba Cuba

Introduction: Currently there are several neurorehabilitation therapies for people with motor impairments caused by neurological disorders. Therapies based on pedaling devices have stood out for their advantages for the recovery of motor function, the prevention or treatment for respiratory and cardiovascular complications, and the induction of neuroplasticity. The Brain Computer Interface (BCI) systems constitute new approaches to provide a rehabilitation that actively involves the patient in therapy and is capable of controlling robotic devices such as stationary bicycles or motorized pedals with their own neural signals. Aim: Reflect the current state of BCI systems that use pedaling devices for the therapy of patients with neurological disorders. Methods: A systematic review was carried out in the PubMed and Science Direct databases and other sources such as IEEEXplore, Sensors magazine and Springer publishing house. Original studies and case reports from the last 10 years based on ICC systems for classification of imaginary pedaling movement from EEG signals were included;in healthy subjects and patients with neurological diseases. Papers that did not show quantitative results from metrics such as accuracy during evaluation were excluded. Risk of bias was assessed using the Critical Appraisal Skills program (CASP). Results: 8 studies that met all eligibility requirements were included. The population of healthy subjects was 43, compared to 6 patients with cerebrovascular accident (CVA) and spinal cord injury (SCI). One of the current limitations of community ICC studies is the scarcity of subjects for the evaluation of different methodologies. The investigations not only yielded quantitative results regarding the performance of the implemented computational algorithms, but also the cortical effects they have produced during cycling as a rehabilitation therapy. Discussion: Various authors have shown that treatment with pedaling devices modulates brain and muscle

关键词： Brain Computer Interface (BCI) Neurological disorders Neurorehabilitation Pedaling devices Pedaling motor imagery

来源：评论

学校读者我要写书评

暂无评论

EEG-Based Multi-Class Classification for Recognizing Pedaling Velocities: A Promising Approach for Brain-Computer Interface-Enhanced Lower-Limb Robotic Rehabilitation

EEG-Based Multi-Class Classification for Recognizing Pedalin...

引用

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： A.X. Gonzalez-Cely C.F. Blanco-Diaz D. Delisle-Rodriguez Teodiano Bastos-Filho Faculty of Electrical Engineering Federal University of Espirito Santo Vitoria-ES Brazil Department of Excellence and AI The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Postgraduate Program in Neuroengineering Edmond and Lily Safra International Institute of Neuroscience Santos Dumont Institute 1560- Zona Rural Macaiba-RN Brazil

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Lower-limb robotic rehabilitation devices have shown promising results in restoring functional activities of individuals with motor impairments. Motorized Mini-Exercise Bikes (MMEBs) are robotic devices that provide functional and repetitive flexion/extension exercises through Passive Pedaling (PP), improving muscular activity, coordination, and functional independence. These technologies activated by Brain-Computer Interfaces (BCIs) can be useful during therapeutic interventions for individuals who cannot voluntarily perform a movement. This study proposes a multi-class classification strategy for recognizing different pedaling velocities using electroencephalography signals. To provide a meaningful task, we conducted a protocol with ten able-bodied subjects who received PP from an MMEB configured at 30, 45, and 60 rpm. We provide a practical demonstration by implementing a feature extraction stage based on Common Spatial Patterns, Power Spectral Density, and a classification system based on four Machine Learning techniques: Linear Discriminant Analysis, Support Vector Machine, k-nearest Neighbors, and Decision Tree. Our strategy achieved a mean accuracy of 0.86, false positive rate of 0.13, and kappa index of 0.64, we concluded that the system has potential to be used in the design of robust controllers for robotic devices. Further research will be centered on real-time evaluation of our strategy to implement an MMEB-based BCI for personalized rehabilitation of people with lower-limb limitations, such as stroke or spinal cord injury survivors.

关键词： Support vector machines Protocols Accuracy Robot kinematics Stroke (medical condition) Feature extraction Real-time systems Electroencephalography Brain-computer interfaces Spinal cord injury

来源：评论

学校读者我要写书评

暂无评论

Real time neural signal processing and visuo-motor integration: New perspectives for assistive technology 16

Real time neural signal processing and visuo-motor integrati...

引用

2014 16th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2014

作者： De Aratanha, Maria Adelia Albano Simplicio, Hougelle Morya, Edgard Moioli, Renan Cipriano Brasil, Fabricio Lima /INCEMAQ Brazil ELS-IINN Postgraduate Program in Neuroengineering Brazil

ISBN: (纸本)9781479966448

I ?Proposers ?Maria Adelia Albano de Aratanha has a BSc. in Electrical Engineering, and a Dipl.-Ing in Biomedical Engineering. Works in the field of Brain Machine Interface (BMI) and virtual reality. ?Hougelle Simpl¨ªcio has a MB in Medicine, and a PhD. in Neurology. Has experience on functional neurosurgery and neuromodulation in both clinical and research field, epilepsy and Parkinson's disease. ?Edgard Morya has BSc. in Physiotherapy, and a PhD in Human Physiology. Has experience on motor control, rehabilitation, plasticity, learning, EMG, EEG, psychophysics, perception-action, neurophysiology, virtual reality, and eye-tracker. ?Renan Cipriano Moioli has a BSc. in Electrical Engineering, a MSc. with emphasis on artificial intelligence and robotics, and Ph.D. in Computational Neuroscience and Robotics. Has experience on computational neuroscience, signal processing, information theory and autonomous robotics. ?Fabr¨ªcio Lima Brasil has a BSc. in Electrical Engineering, a MSc. in Mechanical Engineering, and a Ph.D. in Neuroscience. Has experiences in the field of brain machine interface (BMI), rehabilitation, EEG, plasticity and brain stimulation techniques.

关键词： Conferences SLC12A2 gene ELECTRICAL ENGINEERING Self-Help Devices robotics PhDs computational neuroscience signal processing electrical engineering Neurophysiology mechanical engineering handicapped aids neuroregulation Neurosciences Brain Stimulation Physical Therapy Modalities

来源：评论

学校读者我要写书评

暂无评论

Real time neural signal processing and visuo-motor integration: New perspectives for assistive technology

Real time neural signal processing and visuo-motor integrati...

引用

International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Maria Adelia Albano de Aratanha Hougelle Simplício Edgard Morya Renan Cipriano Moioli Fabricio Lima Brasil Edmond and Lily Safra International Institute of Neuroscience of Natal (ELS-IINN)/INCEMAQ ELS-IINN postgraduate program in neuroengineering

I — Proposers — Maria Adelia Albano de Aratanha has a BSc. in Electrical Engineering, and a Dipl.-Ing in Biomedical Engineering. Works in the field of Brain Machine Interface (BMI) and virtual reality. — Hougelle Simplício has a MB in Medicine, and a PhD. in Neurology. Has experience on functional neurosurgery and neuromodulation in both clinical and research field, epilepsy and Parkinson's disease. — Edgard Morya has BSc. in Physiotherapy, and a PhD in Human Physiology. Has experience on motor control, rehabilitation, plasticity, learning, EMG, EEG, psychophysics, perception-action, neurophysiology, virtual reality, and eye-tracker. — Renan Cipriano Moioli has a BSc. in Electrical Engineering, a MSc. with emphasis on artificial intelligence and robotics, and Ph.D. in Computational Neuroscience and Robotics. Has experience on computational neuroscience, signal processing, information theory and autonomous robotics. — Fabrício Lima Brasil has a BSc. in Electrical Engineering, a MSc. in Mechanical Engineering, and a Ph.D. in Neuroscience. Has experiences in the field of brain machine interface (BMI), rehabilitation, EEG, plasticity and brain stimulation techniques.

关键词： Conferences

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：