Pub Date : 2023-12-07DOI: 10.1080/2326263x.2023.2287719
Holly Wilson, Xi Chen, Mohammad Golbabaee, Michael J. Proulx, Eamonn O'Neill
{"title":"Feasibility of decoding visual information from EEG","authors":"Holly Wilson, Xi Chen, Mohammad Golbabaee, Michael J. Proulx, Eamonn O'Neill","doi":"10.1080/2326263x.2023.2287719","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2287719","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"36 15","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138592246","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}
Pub Date : 2023-11-18DOI: 10.1080/2326263x.2023.2283345
M. Fried-Oken, Michelle Kinsella, Ian Stevens, E. Klein
{"title":"What stakeholders with neurodegenerative conditions value about speech and accuracy in development of BCI systems for communication","authors":"M. Fried-Oken, Michelle Kinsella, Ian Stevens, E. Klein","doi":"10.1080/2326263x.2023.2283345","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2283345","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"89 4","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139261036","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}
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.
{"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}
Pub Date : 2023-09-14DOI: 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.
{"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}
Pub Date : 2023-09-06DOI: 10.1080/2326263x.2023.2247804
Stephanie L. Plummer, Arjang Ahmadpour, John McDaid, Joseph Mark, John M. Lee, Vimal Patel, Julian E. Bailes
{"title":"Subcortical implantation of a passive microchip in rodents – an observational proof-of-concept study","authors":"Stephanie L. Plummer, Arjang Ahmadpour, John McDaid, Joseph Mark, John M. Lee, Vimal Patel, Julian E. Bailes","doi":"10.1080/2326263x.2023.2247804","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2247804","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"33 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73592721","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}
Pub Date : 2023-07-07DOI: 10.1080/2326263x.2023.2233368
Beril Susan Philip, G. Prasad, D. Hemanth
{"title":"A systematic review on artifact removal and classification techniques for enhanced MEG-based BCI systems","authors":"Beril Susan Philip, G. Prasad, D. Hemanth","doi":"10.1080/2326263x.2023.2233368","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2233368","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"29 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77121467","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}
Pub Date : 2023-05-14DOI: 10.1080/2326263x.2023.2206292
M. Jehosheba Margaret, N. M. Masoodhu Banu
{"title":"Performance analysis of EEG based emotion recognition using deep learning models","authors":"M. Jehosheba Margaret, N. M. Masoodhu Banu","doi":"10.1080/2326263x.2023.2206292","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2206292","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"25 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81704942","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}
Pub Date : 2023-02-02DOI: 10.1080/2326263x.2023.2166651
Farzad Saffari, Ali Khadem
{"title":"A deep learning method for classification of steady-state visual evoked potentials in a brain-computer interface speller","authors":"Farzad Saffari, Ali Khadem","doi":"10.1080/2326263x.2023.2166651","DOIUrl":"https://doi.org/10.1080/2326263x.2023.2166651","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"30 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73530314","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}
Pub Date : 2022-11-14DOI: 10.1080/2326263x.2022.2134623
Elham Rostami, F. Ghassemi, Z. Tabanfar
{"title":"Transfer Learning assisted PodNet for Stimulation Frequency Detection in Steady state visually evoked potential-based BCI Spellers","authors":"Elham Rostami, F. Ghassemi, Z. Tabanfar","doi":"10.1080/2326263x.2022.2134623","DOIUrl":"https://doi.org/10.1080/2326263x.2022.2134623","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"43 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87621665","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}
Pub Date : 2022-09-01DOI: 10.1080/2326263x.2022.2114225
Isao Sakamaki, M. Tavakoli, S. Wiebe, K. Adams
{"title":"Examination of effectiveness of kinaesthetic haptic feedback for motor imagery-based brain-computer interface training","authors":"Isao Sakamaki, M. Tavakoli, S. Wiebe, K. Adams","doi":"10.1080/2326263x.2022.2114225","DOIUrl":"https://doi.org/10.1080/2326263x.2022.2114225","url":null,"abstract":"","PeriodicalId":45112,"journal":{"name":"Brain-Computer Interfaces","volume":"115 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84870729","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}
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