1
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Yin X, Yang C, Dong H, Liang J, Lin M. Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI. Med Biol Eng Comput 2024:10.1007/s11517-024-03193-x. [PMID: 39313602 DOI: 10.1007/s11517-024-03193-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 08/22/2024] [Indexed: 09/25/2024]
Abstract
The uncalibrated brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP) can omit the training process and is closer to the practical application. Filter bank canonical correlation analysis (FBCCA), as a classical approach of uncalibrated SSVEP-based BCI, extracts the fundamental and harmonic ingredients through filter bank decomposition. Nevertheless, this method fails to fully leverage the temporal feature of the signal. The paper suggested utilizing reconstructed data with temporal delay in the computation of the canonical correlation coefficient, and the different combinations of the time-delayed embedding and FBCCA were discussed. We selected the data from seven participants in the Benchmark dataset for parameter optimization and evaluated the method across all participants. The experimental results showed that only embedding the time-delayed version into the first subband (FBdCCA) was better than embedding it into all subbands (FBdCCA(all)), and the accuracy of FBdCCA surpassed that of FBCCA significantly. This suggests that the approach of time-delayed embedding can further enhance the performance of FBCCA.
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Affiliation(s)
- Xiangguo Yin
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, Shandong, China
- University of Health and Rehabilitation Sciences, Qingdao, 266071, Shandong, China
| | - Caixiu Yang
- The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Hui Dong
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, Shandong, China
| | - Jingting Liang
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, Shandong, China
| | - Mingxing Lin
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, Shandong, China.
- Shenzhen Research Institute of Shandong University, Shenzhen, 518057, Guangdong, China.
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2
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Yin X, Lin M, Liang J, Zeng F. Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI. Cogn Neurodyn 2024; 18:1733-1741. [PMID: 39104685 PMCID: PMC11297850 DOI: 10.1007/s11571-023-10035-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 06/18/2023] [Accepted: 07/30/2023] [Indexed: 08/07/2024] Open
Abstract
Multivariate synchronization index (MSI), as an effective recognition algorithm for steady-state visual evoked potential (SSVEP) brain-computer interface (BCI), can accurately decode target frequencies without training. To further consider temporal features or extract harmonic components, extended MSI (EMSI), temporally local MSI (TMSI), and filter bank MSI (FBMSI) have been proposed. However, the promotion effects of the above three strategies on MSI have not been compared in detail. In this paper, the performance of EMSI, TMSI, and FBMSI under different time windows was analyzed with the same dataset. The results indicated that the improvement effect of the temporally local method on MSI was better than that of the other two methods under the short time window, and the effect of the filter bank method was better when the time window was greater than 0.8 s. Based on the idea of simultaneously extracting time-frequency features, FBEMSI and FBTMSI were proposed by integrating time delay embedding and temporally local method into FBMSI respectively. The two improved methods, which has no significant difference, can improve the recognition effect of FBMSI. But the computing time of FBEMSI was shorter, which can be a potential method for SSVEP-BCI.
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Affiliation(s)
- Xiangguo Yin
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061 Shandong China
- University of Health and Rehabilitation Sciences, Qingdao, 266071 Shandong China
| | - Mingxing Lin
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061 Shandong China
| | - Jingting Liang
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061 Shandong China
| | - Fanshuo Zeng
- Department of Rehabilitation Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033 Shandong China
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3
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Herbert C. Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance. Front Hum Neurosci 2024; 17:1286895. [PMID: 38435127 PMCID: PMC10904616 DOI: 10.3389/fnhum.2023.1286895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/11/2023] [Indexed: 03/05/2024] Open
Abstract
Brain-computer interfaces (BCIs) are well-known instances of how technology can convert a user's brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user's internal mental states and traits including motivation, affect or cognition, personality traits, or the user's satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user's self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user's self-concept (self-referential knowledge and beliefs about one's self) guide information processing and modulate the user's motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one's self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user's self-concept in the BCI setting - including paradigms, user instructions, and training sessions.
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Affiliation(s)
- Cornelia Herbert
- Department of Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
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4
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Yin X, Lin M. Multi-information improves the performance of CCA-based SSVEP classification. Cogn Neurodyn 2024; 18:165-172. [PMID: 38406193 PMCID: PMC10881948 DOI: 10.1007/s11571-022-09923-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/24/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
The target recognition algorithm based on canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. To reduce visual fatigue and improve the information transfer rate (ITR), how to improve the accuracy of algorithms within a short time window has become one of the main problems at present. There were filter bank CCA (FBCCA), individual template CCA (ITCCA), and temporally local CCA (TCCA), which improve the CCA algorithm from different aspects.This paper proposed to consider individual, frequency, and time information at the same time, so as to extract features more effectively. A comparison of the various methods was performed using benchmark dataset. Classification accuracy and ITR were used for performance evaluation. In the different extensions of CCA, the method incorporating the above three kinds of information simultaneously achieved the best performance within a short time window. This study explores the effect of using a variety of information to improve the CCA algorithm.
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Affiliation(s)
- Xiangguo Yin
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key La-boratory of High-efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engi-neering, Shandong University, Jinan, 250061 China
- University of Health and Rehabilitation Sciences, Qingdao, 266071 China
| | - Mingxing Lin
- National Demonstration Center for Experimental Mechanical Engineering Education (Shandong University), Key La-boratory of High-efficiency and Clean Mechanical Manufacture of Ministry of Education, School of Mechanical Engi-neering, Shandong University, Jinan, 250061 China
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5
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Peksa J, Mamchur D. State-of-the-Art on Brain-Computer Interface Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:6001. [PMID: 37447849 DOI: 10.3390/s23136001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.
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Affiliation(s)
- Janis Peksa
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Institute of Information Technology, Riga Technical University, Kalku Street 1, LV-1658 Riga, Latvia
| | - Dmytro Mamchur
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Computer Engineering and Electronics Department, Kremenchuk Mykhailo Ostrohradskyi National University, Pershotravneva 20, 39600 Kremenchuk, Ukraine
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6
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Riccio A, Schettini F, Galiotta V, Giraldi E, Grasso MG, Cincotti F, Mattia D. Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis. Front Hum Neurosci 2022; 16:868419. [PMID: 35721361 PMCID: PMC9204311 DOI: 10.3389/fnhum.2022.868419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/26/2022] [Indexed: 11/25/2022] Open
Abstract
Brain-computer interface (BCI) can provide people with motor disabilities with an alternative channel to access assistive technology (AT) software for communication and environmental interaction. Multiple sclerosis (MS) is a chronic disease of the central nervous system that mostly starts in young adulthood and often leads to a long-term disability, possibly exacerbated by the presence of fatigue. Patients with MS have been rarely considered as potential BCI end-users. In this pilot study, we evaluated the usability of a hybrid BCI (h-BCI) system that enables both a P300-based BCI and conventional input devices (i.e., muscular dependent) to access mainstream applications through the widely used AT software for communication “Grid 3.” The evaluation was performed according to the principles of the user-centered design (UCD) with the aim of providing patients with MS with an alternative control channel (i.e., BCI), potentially less sensitive to fatigue. A total of 13 patients with MS were enrolled. In session I, participants were presented with a widely validated P300-based BCI (P3-speller); in session II, they had to operate Grid 3 to access three mainstream applications with (1) an AT conventional input device and (2) the h-BCI. Eight patients completed the protocol. Five out of eight patients with MS were successfully able to access the Grid 3 via the BCI, with a mean online accuracy of 83.3% (± 14.6). Effectiveness (online accuracy), satisfaction, and workload were comparable between the conventional AT inputs and the BCI channel in controlling the Grid 3. As expected, the efficiency (time for correct selection) resulted to be significantly lower for the BCI with respect to the AT conventional channels (Z = 0.2, p < 0.05). Although cautious due to the limited sample size, these preliminary findings indicated that the BCI control channel did not have a detrimental effect with respect to conventional AT channels on the ability to operate an AT software (Grid 3). Therefore, we inferred that the usability of the two access modalities was comparable. The integration of BCI with commercial AT input devices to access a widely used AT software represents an important step toward the introduction of BCIs into the AT centers’ daily practice.
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Affiliation(s)
- Angela Riccio
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
- *Correspondence: Angela Riccio,
| | - Francesca Schettini
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Valentina Galiotta
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Enrico Giraldi
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | | | - Febo Cincotti
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
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7
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Antonietti A, Balachandran P, Hossaini A, Hu Y, Valeriani D. The BCI Glossary: a first proposal for a community review. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1969789] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Alberto Antonietti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | | | - Ali Hossaini
- Department of Engineering, King’s College London, London, UK
| | - Yaoping Hu
- Department of Electrical and Software Engineering,University of Calgary, Calgary, AB, Canada
| | - Davide Valeriani
- Department of Otolaryngology Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Otolaryngology Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
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8
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Nam CS, Traylor Z, Chen M, Jiang X, Feng W, Chhatbar PY. Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface. Front Neurorobot 2021; 15:656943. [PMID: 34025383 PMCID: PMC8138057 DOI: 10.3389/fnbot.2021.656943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/22/2021] [Indexed: 12/28/2022] Open
Abstract
This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications. Experimental papers primarily used transcranial magnetic stimulation (tMS) for the CBI portion of their B2BI. Most targeted the visual cortex to produce phosphenes. In terms of study design, 73.3% (11) are unidirectional and 86.7% (13) use only a 1:1 collaboration model (subject to subject). Limitations are apparent, as the CBI method varied greatly between studies indicating no agreed upon neurostimulatory method for transmitting information. Furthermore, only 12.4% (2) studies are more complicated than a 1:1 model and few researchers studied direct bidirectional B2BI. These studies show B2BI can offer advances in human communication and collaboration, but more design and experiments are needed to prove potential. B2BIs may allow rehabilitation therapists to pass information mentally, activating a patient's brain to aid in stroke recovery and adding more complex bidirectionality may allow for increased behavioral synchronization between users. The field is very young, but applications of B2BI technology to neuroergonomics and human factors engineering clearly warrant more research.
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Affiliation(s)
- Chang S. Nam
- Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC, United States
| | - Zachary Traylor
- Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC, United States
| | - Mengyue Chen
- Department of Electrical & Computer Engineering, North Carolina State University, Raleigh, NC, United States
| | - Xiaoning Jiang
- Department of Electrical & Computer Engineering, North Carolina State University, Raleigh, NC, United States
| | - Wuwei Feng
- Department of Neurology, Duke University, Durham, NC, United States
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9
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Kinney-Lang E, Kelly D, Floreani ED, Jadavji Z, Rowley D, Zewdie ET, Anaraki JR, Bahari H, Beckers K, Castelane K, Crawford L, House S, Rauh CA, Michaud A, Mussi M, Silver J, Tuck C, Adams K, Andersen J, Chau T, Kirton A. Advancing Brain-Computer Interface Applications for Severely Disabled Children Through a Multidisciplinary National Network: Summary of the Inaugural Pediatric BCI Canada Meeting. Front Hum Neurosci 2020; 14:593883. [PMID: 33343318 PMCID: PMC7744376 DOI: 10.3389/fnhum.2020.593883] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 11/10/2020] [Indexed: 11/15/2022] Open
Abstract
Thousands of youth suffering from acquired brain injury or other early-life neurological disease live, mature, and learn with only limited communication and interaction with their world. Such cognitively capable children are ideal candidates for brain-computer interfaces (BCI). While BCI systems are rapidly evolving, a fundamental gap exists between technological innovators and the patients and families who stand to benefit. Forays into translating BCI systems to children in recent years have revealed that kids can learn to operate simple BCI with proficiency akin to adults. BCI could bring significant boons to the lives of many children with severe physical impairment, supporting their complex physical and social needs. However, children have been neglected in BCI research and a collaborative BCI research community is required to unite and push pediatric BCI development forward. To this end, the pediatric BCI Canada collaborative network (BCI-CAN) was formed, under a unified goal to cooperatively drive forward pediatric BCI innovation and impact. This article reflects on the topics and discussions raised in the foundational BCI-CAN meeting held in Toronto, ON, Canada in November 2019 and suggests the next steps required to see BCI impact the lives of children with severe neurological disease and their families.
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Affiliation(s)
- Eli Kinney-Lang
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Dion Kelly
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Erica D Floreani
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Zeanna Jadavji
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Danette Rowley
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Ephrem Takele Zewdie
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Javad R Anaraki
- Department of Rehabilitation Science, Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,PRISM Laboratory, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Hosein Bahari
- I CAN Centre, Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada
| | - Kim Beckers
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Karen Castelane
- PRISM Laboratory, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Lindsey Crawford
- PRISM Laboratory, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Sarah House
- PRISM Laboratory, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Chelsea A Rauh
- PRISM Laboratory, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Amber Michaud
- I CAN Centre, Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada
| | - Matheus Mussi
- Assistive Technology Laboratory, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Jessica Silver
- PRISM Laboratory, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Corinne Tuck
- I CAN Centre, Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada
| | - Kim Adams
- Assistive Technology Laboratory, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - John Andersen
- I CAN Centre, Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada.,Assistive Technology Laboratory, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Tom Chau
- Department of Rehabilitation Science, Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.,PRISM Laboratory, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Adam Kirton
- Department of Pediatrics and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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10
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Kinney-Lang E, Murji S, Kelly D, Paffrath B, Zewdie E, Kirton A. Designing a flexible tool for rapid implementation of brain-computer interfaces (BCI) in game development. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:6078-6081. [PMID: 33019357 DOI: 10.1109/embc44109.2020.9175801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Early neurological injury or disease can lead to severe life-long physical impairments, despite normal cognitive function. For such individuals, brain-computer interfaces (BCI) may provide a means to regain access to the world by offering control of systems through directly processing brain patterns. However, current BCI applications are often research driven and consequently seen as uninteresting, particularly for prolonged use and younger BCI-users. To help mitigate this concern, this paper establishes a tool for researchers and game developers alike to rapidly incorporate a BCI control scheme (the P300 oddball response) into a gaming environment. Preliminary results indicate the proposed P300 Dynamic Cube (PDC) asset works in online BCI environments (n=20, healthy adult participants), resulting in median classification accuracy of 75 ± 3.28%. Additionally, the PDC tool can be rapidly adapted for a variety of game designs, evidenced by its incorporation into submissions to the Brain-Computer Interface (BCI) Game Jam 2019 competition. These findings support the PDC as a useful asset in the design and development of BCI-based games.
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11
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Abstract
Locked-in syndrome (LIS) is characterized by an inability to move or speak in the presence of intact cognition and can be caused by brainstem trauma or neuromuscular disease. Quality of life (QoL) in LIS is strongly impaired by the inability to communicate, which cannot always be remedied by traditional augmentative and alternative communication (AAC) solutions if residual muscle activity is insufficient to control the AAC device. Brain-computer interfaces (BCIs) may offer a solution by employing the person's neural signals instead of relying on muscle activity. Here, we review the latest communication BCI research using noninvasive signal acquisition approaches (electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy) and subdural and intracortical implanted electrodes, and we discuss current efforts to translate research knowledge into usable BCI-enabled communication solutions that aim to improve the QoL of individuals with LIS.
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12
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Ji N, Ma L, Dong H, Zhang X. EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy. Brain Sci 2019; 9:E201. [PMID: 31416258 PMCID: PMC6721346 DOI: 10.3390/brainsci9080201] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 08/06/2019] [Accepted: 08/12/2019] [Indexed: 11/17/2022] Open
Abstract
The classification recognition rate of motor imagery is a key factor to improve the performance of brain-computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the sub-band signal is decomposed with EMD to get a set of stationary time series, which are called intrinsic mode functions (IMFs). Secondly, the appropriate IMFs for signal reconstruction are selected. Thus, the approximate entropy of the reconstructed signal can be obtained as the corresponding feature vector. Finally, support vector machine (SVM) is used to perform the classification. The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging.
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Affiliation(s)
- Na Ji
- College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, 210023 Nanjing, China
| | - Liang Ma
- College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, 210023 Nanjing, China
| | - Hui Dong
- College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, 210023 Nanjing, China
| | - Xuejun Zhang
- College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, 210023 Nanjing, China.
- Nation-Local Joint Project Engineering Lab of RF Integration & Micropackage, Nanjing University of Posts and Telecommunications, 210023 Nanjing, China.
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13
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Abstract
Brain-Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.
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Affiliation(s)
- D J McFarland
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA
| | - J R Wolpaw
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA
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14
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Choi I, Rhiu I, Lee Y, Yun MH, Nam CS. A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives. PLoS One 2017; 12:e0176674. [PMID: 28453547 PMCID: PMC5409179 DOI: 10.1371/journal.pone.0176674] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.
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Affiliation(s)
- Inchul Choi
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Ilsun Rhiu
- Division of Global Management Engineering, Hoseo University, Asan, Korea
| | - Yushin Lee
- Department of Industrial Engineering, Seoul National University, Seoul, Korea
| | - Myung Hwan Yun
- Department of Industrial Engineering, Seoul National University, Seoul, Korea
| | - Chang S. Nam
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, United States of America
- * E-mail:
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15
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Congedo M, Barachant A, Bhatia R. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. BRAIN-COMPUTER INTERFACES 2017. [DOI: 10.1080/2326263x.2017.1297192] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Marco Congedo
- GIPSA-lab, CNRS, Grenoble Institute of Technology, Grenoble Alpes University, Grenoble, France
| | - Alexandre Barachant
- Early Brain Injury and Recovery Lab, Burke Medical Research Institute, White Plains, NY, USA
| | - Rajendra Bhatia
- Department of Theoretical Statistics and Mathematics, Indian Statistical Institute, New Delhi, India
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16
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Kinney-Lang E, Auyeung B, Escudero J. Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain–computer interfaces for motor rehabilitation in children. J Neural Eng 2016; 13:061002. [DOI: 10.1088/1741-2560/13/6/061002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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17
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Specker Sullivan L, Illes J. Beyond ‘communication and control’: towards ethically complete rationales for brain-computer interface research. BRAIN-COMPUTER INTERFACES 2016. [DOI: 10.1080/2326263x.2016.1213603] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Laura Specker Sullivan
- National Core for Neuroethics, University of British Columbia, Vancouver, Canada
- Center for Sensorimotor Neural Engineering, University of Washington, Box 37, 1414 NE 42nd Street Suite 204, Seattle, WA 98105-6271, USA
- Department of Philosophy, University of Washington, USA
| | - Judy Illes
- National Core for Neuroethics, University of British Columbia, Vancouver, Canada
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Riccio A, Pichiorri F, Schettini F, Toppi J, Risetti M, Formisano R, Molinari M, Astolfi L, Cincotti F, Mattia D. Interfacing brain with computer to improve communication and rehabilitation after brain damage. PROGRESS IN BRAIN RESEARCH 2016; 228:357-87. [PMID: 27590975 DOI: 10.1016/bs.pbr.2016.04.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.
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Affiliation(s)
- A Riccio
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - F Pichiorri
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - F Schettini
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - J Toppi
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - M Risetti
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - R Formisano
- Post-Coma Unit, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - M Molinari
- Spinal Cord Unit, IRCCS Santa Lucia Foundation, Rome, Italy
| | - L Astolfi
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - F Cincotti
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy; Sapienza University of Rome, Rome, Italy
| | - D Mattia
- Neuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, Italy.
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