Brain-Computer Interfaces最新文献

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":"39 1","pages":"0"},"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":"39 1","pages":"0"},"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

A systematic review on artifact removal and classification techniques for enhanced MEG-based BCI systems 基于增强型微机的脑机接口系统的伪影去除和分类技术综述

IF 2.1

Brain-Computer Interfaces Pub Date : 2023-07-07 DOI: 10.1080/2326263x.2023.2233368

Beril Susan Philip, G. Prasad, D. Hemanth

引用次数: 1

Performance analysis of EEG based emotion recognition using deep learning models 基于深度学习模型的EEG情绪识别性能分析

IF 2.1

Brain-Computer Interfaces Pub Date : 2023-05-14 DOI: 10.1080/2326263x.2023.2206292

M. Jehosheba Margaret, N. M. Masoodhu Banu

引用次数: 0

A deep learning method for classification of steady-state visual evoked potentials in a brain-computer interface speller 基于深度学习的脑机接口拼字器稳态视觉诱发电位分类方法

IF 2.1

Brain-Computer Interfaces Pub Date : 2023-02-02 DOI: 10.1080/2326263x.2023.2166651

Farzad Saffari, Ali Khadem

引用次数: 1

Transfer Learning assisted PodNet for Stimulation Frequency Detection in Steady state visually evoked potential-based BCI Spellers 迁移学习辅助PodNet在稳态视觉诱发电位BCI拼写者刺激频率检测中的应用

IF 2.1

Brain-Computer Interfaces Pub Date : 2022-11-14 DOI: 10.1080/2326263x.2022.2134623

Elham Rostami, F. Ghassemi, Z. Tabanfar

引用次数: 0

Examination of effectiveness of kinaesthetic haptic feedback for motor imagery-based brain-computer interface training 动觉触觉反馈在基于运动图像的脑机接口训练中的有效性检验

IF 2.1

Brain-Computer Interfaces Pub Date : 2022-09-01 DOI: 10.1080/2326263x.2022.2114225

Isao Sakamaki, M. Tavakoli, S. Wiebe, K. Adams

引用次数: 2