Brain-Computer Interfaces最新文献

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

Brain-Computer Interfaces Pub Date : 2024-07-22 DOI: 10.1080/2326263x.2024.2379009

Natasha Padfield, Tracey Camilleri, Simon Fabri, Marvin Bugeja, Kenneth Camilleri

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

Brain-Computer Interfaces 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

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

Brain-Computer Interfaces 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

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

Brain-Computer Interfaces Pub Date : 2024-05-02 DOI: 10.1080/2326263x.2024.2347790

Vishnupriya R, Neethu Robinson, Ramasubba Reddy M

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

Brain-Computer Interfaces 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

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

Brain-Computer Interfaces Pub Date : 2023-12-07 DOI: 10.1080/2326263x.2023.2287719

Holly Wilson, Xi Chen, Mohammad Golbabaee, Michael J. Proulx, Eamonn O'Neill

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

Brain-Computer Interfaces Pub Date : 2023-11-18 DOI: 10.1080/2326263x.2023.2283345

M. Fried-Oken, Michelle Kinsella, Ian Stevens, E. Klein

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

Brain-Computer Interfaces Pub Date : 2023-10-18 DOI: 10.1080/2326263x.2023.2264000

Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos

{"title":"Effect of head-mounted virtual reality and vibrotactile feedback in ERD during motor imagery Brain–computer interface training","authors":"Diogo Batista, Gustavo Caetano, Mathis Fleury, Patricia Figueiredo, Athanasios Vourvopoulos","doi":"10.1080/2326263x.2023.2264000","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2264000","url":null,"abstract":"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.","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135889137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Closed loop BCI system for Cybathlon 2020 2020 Cybathlon闭环BCI系统

Brain-Computer Interfaces Pub Date : 2023-09-14 DOI: 10.1080/2326263x.2023.2254463

Csaba Köllőd, András Adolf, Gergely Márton, Moutz Wahdow, Ward Fadel, István Ulbert

{"title":"Closed loop BCI system for Cybathlon 2020","authors":"Csaba Köllőd, András Adolf, Gergely Márton, Moutz Wahdow, Ward Fadel, István Ulbert","doi":"10.1080/2326263x.2023.2254463","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2254463","url":null,"abstract":"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.","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134970101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Brain-Computer Interfaces 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

引用次数: 0