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Brain-Computer Interfaces最新文献

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A combined EEG motor and speech imagery paradigm with automated successive halving for customizable command selection 结合脑电图运动和语言图像范例,自动连续减半,可自定义指令选择
IF 1.8 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-07-22 DOI: 10.1080/2326263x.2024.2379009
Natasha Padfield, Tracey Camilleri, Simon Fabri, Marvin Bugeja, Kenneth Camilleri
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引用次数: 0
Toward methodologies for motor imagery enhancement: a tDCS-BCI study 运动想象增强方法:tDCS-BCI 研究
IF 1.8 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2024-07-03 DOI: 10.1080/2326263x.2024.2372863
Diego Andrés Blanco Mora, S. Van Hoornweder, Kim van Dun, Stefanie Verstraelen, K. Cuypers, Sergi Bermúdez i Badia, R. Meesen
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引用次数: 0
Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces 大规模调查性别对运动图像脑机接口μ节奏抑制的影响
IF 2.1 Q2 Engineering Pub Date : 2024-05-04 DOI: 10.1080/2326263x.2024.2345449
Valentina Gamboa von Groll, Nikki Leeuwis, Sébastien Rimbert, A. Roc, Léa Pillette, Fabien Lotte, Maryam Alimardani
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引用次数: 0
Genetic algorithm based deep learning model adaptation for improvising the motor imagery classification 基于遗传算法的深度学习模型适应性改进运动图像分类
IF 2.1 Q2 Engineering Pub Date : 2024-05-02 DOI: 10.1080/2326263x.2024.2347790
Vishnupriya R, Neethu Robinson, Ramasubba Reddy M
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引用次数: 0
Real-time feedback improves imagined 3D primitive object classification from EEG 实时反馈改进了脑电图的三维原始物体分类想象
IF 2.1 Q2 Engineering Pub Date : 2024-03-27 DOI: 10.1080/2326263x.2024.2334558
A. Korik, Naomi du Bois, G. Campbell, Eamonn O’Neill, Laura Hay, Sam Gilbert, Madeleine Grealy, Damien Coyle
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引用次数: 0
Feasibility of decoding visual information from EEG 从脑电图解码视觉信息的可行性
IF 2.1 Q2 Engineering Pub Date : 2023-12-07 DOI: 10.1080/2326263x.2023.2287719
Holly Wilson, Xi Chen, Mohammad Golbabaee, Michael J. Proulx, Eamonn O'Neill
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引用次数: 0
What stakeholders with neurodegenerative conditions value about speech and accuracy in development of BCI systems for communication 在开发用于通信的生物识别(BCI)系统时,患有神经退行性疾病的利益相关者对语音和准确性的重视程度
IF 2.1 Q2 Engineering Pub Date : 2023-11-18 DOI: 10.1080/2326263x.2023.2283345
M. Fried-Oken, Michelle Kinsella, Ian Stevens, E. Klein
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引用次数: 0
Effect of head-mounted virtual reality and vibrotactile feedback in ERD during motor imagery Brain–computer interface training 头戴式虚拟现实与振动触觉反馈在运动图像脑机接口训练中的作用
Q2 Engineering Pub Date : 2023-10-18 DOI: 10.1080/2326263x.2023.2264000
Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos
Brain–computer interfaces (BCIs) can provide a non-muscular channel of control to stroke patients for motor rehabilitation. This can be achieved through the use of motor imagery (MI) training, involving the modulation of sensorimotor rhythms. The practice of MI has been shown to be able to strengthen key motor pathways when reinforced with rewarding feedback. Recently, there has been a growing evidence of the positive impact of embodied virtual reality (VR) and vibrotactile feedback in MI training. Nonetheless, it is not yet clear what the optimal MI-BCI setup is for evoking stronger sensorimotor rhythms in VR. In this study, we investigate the impact of head-mounted VR, and vibrotactile feedback during MI-BCI training in the induced sensorimotor rhythms. To achieve this, 19 healthy subjects performed MI training with embodied VR between four conditions: head-mounted vs. screen VR, with and without vibrotactile feedback; and two control conditions: abstract MI without embodied feedback, and motor execution. The event-related desynchronization (ERD) and the lateralization indices (LI) of the Alpha and Beta EEG rhythms were analyzed in a within-subject design. Results show that the combination of vibrotactile feedback and embodied VR can induce stronger and more lateralized Alpha ERD; nonetheless, LI was not significantly different across conditions.
脑机接口(bci)可为脑卒中患者的运动康复提供非肌肉控制通道。这可以通过使用运动意象(MI)训练来实现,包括感觉运动节奏的调节。MI的练习已经被证明能够在奖励反馈的强化下加强关键的运动通路。近年来,越来越多的证据表明,具身虚拟现实(VR)和振动触觉反馈在心肌梗死训练中的积极影响。尽管如此,目前尚不清楚在VR中唤起更强烈的感觉运动节奏的最佳MI-BCI设置是什么。在这项研究中,我们研究了头戴式VR和振动触觉反馈在MI-BCI训练中对诱导的感觉运动节奏的影响。为了实现这一目标,19名健康受试者在四种情况下进行了嵌入VR的MI训练:头戴式VR和屏幕VR,有和没有振动触觉反馈;两种控制条件:无具身反馈的抽象MI和运动执行。采用受试者内设计分析了脑电节律的事件相关去同步(ERD)和侧化指数(LI)。结果表明,振动触觉反馈与具身VR相结合可诱导更强、更偏侧的Alpha ERD;然而,不同条件下的LI没有显著差异。
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引用次数: 0
Closed loop BCI system for Cybathlon 2020 2020 Cybathlon闭环BCI系统
Q2 Engineering Pub Date : 2023-09-14 DOI: 10.1080/2326263x.2023.2254463
Csaba Köllőd, András Adolf, Gergely Márton, Moutz Wahdow, Ward Fadel, István Ulbert
We developed a Brain-Computer Interface (BCI) System for the BCI discipline of Cybathlon 2020 competition, where participants with tetraplegia (pilots) control a computer game with mental commands. To extract features from one-second-long electroencephalographic (EEG) signals, we calculated the absolute of the Fast-Fourier Transformation amplitude (FFTabs) and introduced two methods: Feature Average and Feature Range. The former calculates the average of the FFTabs for a specific frequency band, while the later generates multiple Feature Averages for non-overlapping 2 Hz wide frequency bins. The resulting features were fed to a Support Vector Machine classifier and tested on the PhysioNet database and our dataset containing 16 offline experiments recorded with the help of 2 pilots. 27 gameplay trials (out of 59) with our pilots reached the 240-second qualification time limit, which demonstrates the usability of our system in real-time circumstances. We critically compared the Feature Average of canonical frequency bands (alpha, beta, gamma, and theta) with our suggested range30 and range40 methods. On the PhysioNet dataset, the range40 method combined with an ensemble SVM classifier significantly reached the highest accuracy level (0.4607), with a 4-class classification; moreover, it outperformed the state-of-the-art EEGNet.
我们为Cybathlon 2020比赛的BCI学科开发了脑机接口(BCI)系统,四肢瘫痪的参与者(飞行员)通过心理命令控制电脑游戏。为了从1秒长的脑电图(EEG)信号中提取特征,我们计算了快速傅立叶变换幅度(FFTabs)的绝对值,并引入了特征平均(Feature Average)和特征范围(Feature Range)两种方法。前者计算特定频带的FFTabs的平均值,而后者为不重叠的2hz宽频带生成多个Feature average。得到的特征被输入到支持向量机分类器中,并在PhysioNet数据库和我们的数据集上进行测试,其中包含16个离线实验,由2个飞行员记录。我们的飞行员进行了27次玩法试验(共59次),达到了240秒的资格时间限制,这证明了我们的系统在实时环境中的可用性。我们将典型频带(alpha、beta、gamma和theta)的Feature Average与我们建议的range30和range40方法进行了严格的比较。在PhysioNet数据集上,range40方法结合集成SVM分类器显著达到最高的准确率水平(0.4607),达到4类分类;此外,它的性能优于最先进的EEGNet。
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引用次数: 1
Subcortical implantation of a passive microchip in rodents – an observational proof-of-concept study 在啮齿类动物的皮质下植入被动微芯片-一项观察性概念验证研究
IF 2.1 Q2 Engineering Pub Date : 2023-09-06 DOI: 10.1080/2326263x.2023.2247804
Stephanie L. Plummer, Arjang Ahmadpour, John McDaid, Joseph Mark, John M. Lee, Vimal Patel, Julian E. Bailes
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引用次数: 0
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Brain-Computer Interfaces
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