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Herbert C, Northoff G. Editorial: Analyzing and computing humans - the role of language, culture, brain and health. Front Hum Neurosci 2024; 18:1439729. [PMID: 39015823 PMCID: PMC11250248 DOI: 10.3389/fnhum.2024.1439729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 06/06/2024] [Indexed: 07/18/2024] Open
Affiliation(s)
- Cornelia Herbert
- Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Faculty of Engineering, Computer Science and Psychology, Ulm University, Ulm, Germany
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
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2
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Yaeger K, Mocco J. Venous Sinus Stent to Treat Paralysis. Neurosurg Clin N Am 2024; 35:375-378. [PMID: 38782530 DOI: 10.1016/j.nec.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Transvenous treatment of paralysis is a concept less than a decade old. The Stentrode (Synchron, Inc, New York, USA) is a novel electrode on stent device intended to be implanted in the superior sagittal sinus adjacent to the motor cortex. Initial animal studies in sheep demonstrated the safety of the implant as well as its accuracy in detecting neural signals at both short and long term. Early human trials have shown the safety of the device and demonstrated the use of the Stentrode system in facilitating patients with paralysis to carry out daily activities such as texting, email, and personal finance. This is an emerging technology with promise, although certainly more research is required to better understand the capabilities and limitations of the device.
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Affiliation(s)
- Kurt Yaeger
- Department of Neurological Surgery, Houston Methodist Hospital, 6560 Fannin Street, Suite 944, Houston, TX 77030, USA.
| | - J Mocco
- Department of Neurological Surgery, Mount Sinai Hospital, 1 Gustave Levy Place, New York, NY 10029, USA
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3
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Falcon-Caro A, Shirani S, Ferreira JF, Bird JJ, Sanei S. Formulation of Common Spatial Patterns for Multi-Task Hyperscanning BCI. IEEE Trans Biomed Eng 2024; 71:1950-1957. [PMID: 38252565 DOI: 10.1109/tbme.2024.3356665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
This work proposes a new formulation for common spatial patterns (CSP), often used as a powerful feature extraction technique in brain-computer interfacing (BCI) and other neurological studies. In this approach, applied to multiple subjects' data and named as hyperCSP, the individual covariance and mutual correlation matrices between multiple simultaneously recorded subjects' electroencephalograms are exploited in the CSP formulation. This method aims at effectively isolating the common motor task between multiple heads and alleviate the effects of other spurious or undesired tasks inherently or intentionally performed by the subjects. This technique can provide a satisfactory classification performance while using small data size and low computational complexity. By using the proposed hyperCSP followed by support vector machines classifier, we obtained a classification accuracy of 81.82% over 8 trials in the presence of strong undesired tasks. We hope that this method could reduce the training error in multi-task BCI scenarios. The recorded valuable motor-related hyperscanning dataset is available for public use to promote the research in this area.
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Fukushi T. East Asian perspective of responsible research and innovation in neurotechnology. IBRO Neurosci Rep 2024; 16:582-597. [PMID: 38774060 PMCID: PMC11107355 DOI: 10.1016/j.ibneur.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/10/2024] [Accepted: 04/30/2024] [Indexed: 05/24/2024] Open
Abstract
After more than half a century of research and development (R&D), Brain-computer interface (BCI)-based Neurotechnology continues to progress as one of the leading technologies of the 2020 s worldwide. Various reports and academic literature in Europe and the United States (U.S.) have outlined the trends in the R&D of neurotechnology and the consideration of ethical issues, and the importance of the formulation of ethical principles, guidance and industrial standards as well as the development of relevant human resources has been discussed. However, limited number studies have focused on neurotechnology R&D, the dissemination of neuroethics related to the academic foundation advancing the discussion on ethical principles, guidance and standards or human resource development in the Asian region. This study fills in this gap in understanding of Eastern Asian (China, Korea and Japan) situation based on the participation in activities to develop ethical principles, guidance, and industrial standards for appropriate use of neurotechnology, in addition to literature survey and clinical registries' search investigation reflecting the trends in neurotechnology R&D as well as its social implication in Asian region. The current study compared the results with the situation in Europa and the U.S. and discussed issues that need to be addressed in the future and discussed the significance and potential of corporate consortium initiatives in Japan and examples of ethics and governance activities in Asian Countries.
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Affiliation(s)
- Tamami Fukushi
- Faculty of Human Welfare, Tokyo Online University, Nishi-Shinjuku Shinjuku-ku, Tokyo 160-0023, Japan
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Brandt-Rauf PW, Ayaz H. Occupational Health and Neuroergonomics: The Future of Wearable Neurotechnologies at the Workplace. J Occup Environ Med 2024; 66:456-460. [PMID: 38829949 DOI: 10.1097/jom.0000000000003080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Affiliation(s)
- Paul W Brandt-Rauf
- From the School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania
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Huang D, Wang Y, Fan L, Yu Y, Zhao Z, Zeng P, Wang K, Li N, Shen H. Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface. Brain Sci 2024; 14:498. [PMID: 38790476 PMCID: PMC11120245 DOI: 10.3390/brainsci14050498] [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: 04/15/2024] [Revised: 05/06/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time-frequency maps using continuous wavelet transform. Based on these time-frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.
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Affiliation(s)
- Dingyong Huang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Yingjie Wang
- College of Physical Education and Health, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China;
| | - Liangwei Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Yang Yu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Ziyu Zhao
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Pu Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Kunqing Wang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
| | - Na Li
- Radiology Department, Xiangya 3rd Hospital, Central South University, Changsha 410013, China;
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; (D.H.); (L.F.); (Y.Y.); (Z.Z.); (P.Z.); (K.W.)
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7
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Proverbio AM, Cesati F. Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study. Front Psychiatry 2024; 15:1357770. [PMID: 38638416 PMCID: PMC11024723 DOI: 10.3389/fpsyt.2024.1357770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. Methods The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Results Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. Discussion In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.
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Affiliation(s)
- Alice Mado Proverbio
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
- NEURO-MI Milan Center for Neuroscience, Milan, Italy
| | - Federico Cesati
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
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Chen Y, Wang F, Li T, Zhao L, Gong A, Nan W, Ding P, Fu Y. Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces. Front Hum Neurosci 2024; 18:1391550. [PMID: 38601800 PMCID: PMC11004276 DOI: 10.3389/fnhum.2024.1391550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.
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Affiliation(s)
- Yanxiao Chen
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Tianwen Li
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Lei Zhao
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, China
| | - Wenya Nan
- Department of Psychology, School of Education, Shanghai Normal University, Shanghai, China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
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Gu M, Pei W, Gao X, Wang Y. Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1090-1099. [PMID: 38437148 DOI: 10.1109/tnsre.2024.3372594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared the characteristics of SSVEP and user experience of different stimulation paradigms with a wide stimulation frequency range of 1-60 Hz. The findings suggest that, for a better balance between system performance and user experience, ON and OFF grid stimuli with a Weber contrast of 50% can be utilized as alternatives to traditional flickering stimulation paradigms in the frequency band of 1-25 Hz. In the 25-35 Hz range, uniform flicker stimuli with the same 50% contrast are more suitable. In the higher frequency band, traditional uniform flicker stimuli with a high 300% contrast are preferred. These results are significant for developing high performance and user-friendly SSVEP-based BCI systems.
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10
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Valentim WL, Tylee DS, Polimanti R. A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices. WIREs Mech Dis 2024; 16:e1635. [PMID: 38059513 PMCID: PMC11163995 DOI: 10.1002/wsbm.1635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/22/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023]
Abstract
Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.
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Affiliation(s)
- Wander L. Valentim
- Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Daniel S. Tylee
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
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Xia H, Zhang Y, Rajabi N, Taleb F, Yang Q, Kragic D, Li Z. Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement. Nat Commun 2024; 15:1760. [PMID: 38409128 PMCID: PMC10897332 DOI: 10.1038/s41467-024-46249-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/19/2024] [Indexed: 02/28/2024] Open
Abstract
Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.
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Affiliation(s)
- Haisheng Xia
- School of Mechanical Engineering, Tongji University, Shanghai, 201804, China
- Translational Research Center, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University, Shanghai, 201619, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230026, China
| | - Yuchong Zhang
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Nona Rajabi
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Farzaneh Taleb
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Qunting Yang
- Department of Automation, University of Science and Technology of China, Hefei, 230026, China
| | - Danica Kragic
- Robotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden
| | - Zhijun Li
- School of Mechanical Engineering, Tongji University, Shanghai, 201804, China.
- Translational Research Center, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Tongji University, Shanghai, 201619, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230026, China.
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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13
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Tucci DL. NIDCD's 5-year strategic plan seeks innovations in assistive device technologies. J Neural Eng 2024; 21:013002. [PMID: 38113536 PMCID: PMC10905644 DOI: 10.1088/1741-2552/ad171b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/19/2023] [Indexed: 12/21/2023]
Affiliation(s)
- Debara L Tucci
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States of America
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14
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Wang X, Liu A, Wu L, Guan L, Chen X. Improving Generalized Zero-Shot Learning SSVEP Classification Performance From Data-Efficient Perspective. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4135-4145. [PMID: 37824324 DOI: 10.1109/tnsre.2023.3324148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into 'seen' (those with training data) and 'unseen' classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.
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15
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Polevaya SA, Parin SB, Fedotchev AI. Combination of EEG-Guided Adaptive Neurostimulation with Resonance Scanning in Correction of Stress-Induced States and Cognitive Rehabilitation of University Students. Bull Exp Biol Med 2023; 175:757-761. [PMID: 37987944 DOI: 10.1007/s10517-023-05940-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Indexed: 11/22/2023]
Abstract
The correction of stress-induced states and cognitive rehabilitation were carried out during the examination session in three university student groups comparable in the number, sex, and age. In the experimental group, a combination of EEG-guided adaptive neurostimulation with preliminary resonance scanning was used. In control group 1, only EEG-guided adaptive neurostimulation was used. In control group 2, musical-acoustic stimuli were presented without feedback from the subject's EEG. Experiments with preliminary resonance scanning revealed the maximum positive effects compared to the two control types of stimulation. A significant increase in the power of EEG rhythms, especially in the alpha range, was accompanied by a significant increase in subjective indicators of the functional state and cognitive activity. These results can be explained from the standpoint of the progressive involvement of the resonant, integrative, and neuroplasticity mechanisms of the brain into the processes of normalization the functional state of the body under the influence of combined stimulation procedures.
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Affiliation(s)
- S A Polevaya
- National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - S B Parin
- National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - A I Fedotchev
- Institute of Cell Biophysics, Russian Academy of Sciences - Separated Subdivision of the Federal Research Center Pushchino Scientific Center of Biological Research, Russian Academy of Sciences, Pushchino, Moscow region, Russia.
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Karikari E, Koshechkin KA. Review on brain-computer interface technologies in healthcare. Biophys Rev 2023; 15:1351-1358. [PMID: 37974976 PMCID: PMC10643750 DOI: 10.1007/s12551-023-01138-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 08/31/2023] [Indexed: 11/19/2023] Open
Abstract
Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.
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Affiliation(s)
- Evelyn Karikari
- Department of Public Health and Healthcare, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Konstantin A Koshechkin
- The Digital Health Institute, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
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17
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Zhang R, Dong G, Li M, Tang Z, Chen X, Cui H. A Calibration-Free Hybrid BCI Speller System Based on High-Frequency SSVEP and sEMG. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3492-3500. [PMID: 37624717 DOI: 10.1109/tnsre.2023.3308779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Hybrid brain-computer interface (hBCI) systems that combine steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) signals have attracted attention of researchers due to the advantage of exhibiting significantly improved system performance. However, almost all existing studies adopt low-frequency SSVEP to build hBCI. It produces much more visual fatigue than high-frequency SSVEP. Therefore, the current study attempts to build a hBCI based on high-frequency SSVEP and sEMG. With these two signals, this study designed and realized a 32-target hBCI speller system. Thirty-two targets were separated from the middle into two groups. Each side contained 16 sets of targets with different high-frequency visual stimuli (i.e., 31-34.75 Hz with an interval of 0.25 Hz). sEMG was utilized to choose the group and SSVEP was adopted to identify intra-group targets. The filter bank canonical correlation analysis (FBCCA) and the root mean square value (RMS) methods were used to identify signals. Therefore, the proposed system allowed users to operate it without system calibration. A total of 12 healthy subjects participated in online experiment, with an average accuracy of 93.52 ± 1.66% and the average information transfer rate (ITR) reached 93.50 ± 3.10 bits/min. Furthermore, 12 participants perfectly completed the free-spelling tasks. These results of the experiments indicated feasibility and practicality of the proposed hybrid BCI speller system.
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18
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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19
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Pérez-Benítez JA, Martínez-Ortiz P, Aguila-Muñoz J. A Review of Formulations, Boundary Value Problems and Solutions for Numerical Computation of Transcranial Magnetic Stimulation Fields. Brain Sci 2023; 13:1142. [PMID: 37626498 PMCID: PMC10452852 DOI: 10.3390/brainsci13081142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
Since the inception of the transcranial magnetic stimulation (TMS) technique, it has become imperative to numerically compute the distribution of the electric field induced in the brain. Various models of the coil-brain system have been proposed for this purpose. These models yield a set of formulations and boundary conditions that can be employed to calculate the induced electric field. However, the literature on TMS simulation presents several of these formulations, leading to potential confusion regarding the interpretation and contribution of each source of electric field. The present study undertakes an extensive compilation of widely utilized formulations, boundary value problems and numerical solutions employed in TMS fields simulations, analyzing the advantages and disadvantages associated with each used formulation and numerical method. Additionally, it explores the implementation strategies employed for their numerical computation. Furthermore, this work provides numerical expressions that can be utilized for the numerical computation of TMS fields using the finite difference and finite element methods. Notably, some of these expressions are deduced within the present study. Finally, an overview of some of the most significant results obtained from numerical computation of TMS fields is presented. The aim of this work is to serve as a guide for future research endeavors concerning the numerical simulation of TMS.
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Affiliation(s)
- J. A. Pérez-Benítez
- Laboratorio de Bio-Electromagnetismo, ESIME-SEPI, Edif. Z-4, Instituto Politécnico Nacional, Mexico City 07738, CDMX, Mexico;
| | - P. Martínez-Ortiz
- Laboratorio de Bio-Electromagnetismo, ESIME-SEPI, Edif. Z-4, Instituto Politécnico Nacional, Mexico City 07738, CDMX, Mexico;
| | - J. Aguila-Muñoz
- CONAHCYT—Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México, km 107 Carretera Tijuana-Ensenada, Apartado Postal 14, Ensenada 22800, BC, Mexico
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20
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Lin J, Lai D, Wan Z, Feng L, Zhu J, Zhang J, Wang Y, Xu K. Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals. Front Hum Neurosci 2023; 17:1168017. [PMID: 37388414 PMCID: PMC10304012 DOI: 10.3389/fnhum.2023.1168017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/24/2023] [Indexed: 07/01/2023] Open
Abstract
Introduction In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs). Methods The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands. Results The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals. Discussion The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology. Clinical trial registration https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.
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Affiliation(s)
- Jiafan Lin
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Dongrong Lai
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zijun Wan
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | | | - Junming Zhu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Jianmin Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Yueming Wang
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
| | - Kedi Xu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory for Biomedical Engineering of Ministry of Education, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, China
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21
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Cardona-Álvarez YN, Álvarez-Meza AM, Cárdenas-Peña DA, Castaño-Duque GA, Castellanos-Dominguez G. A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments. SENSORS (BASEL, SWITZERLAND) 2023; 23:3763. [PMID: 37050823 PMCID: PMC10098804 DOI: 10.3390/s23073763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.
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Affiliation(s)
| | | | | | - Germán Albeiro Castaño-Duque
- Cultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
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22
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Saibene A, Caglioni M, Corchs S, Gasparini F. EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:2798. [PMID: 36905004 PMCID: PMC10007053 DOI: 10.3390/s23052798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.
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Affiliation(s)
- Aurora Saibene
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
| | - Mirko Caglioni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Silvia Corchs
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
- Department of Theoretical and Applied Sciences, University of Insubria, Via J. H. Dunant 3, 21100 Varese, Italy
| | - Francesca Gasparini
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
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23
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Shah AM. New development in brain-computer interface platforms: 1-year results from the SWITCH trial. Artif Organs 2023; 47:615-616. [PMID: 36861900 DOI: 10.1111/aor.14511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Synchron publishes SWITCH trial results demonstrating the safety and efficacy of stentrode™ device. The stentrode™ is an endovascularly implanted brain-computer interface communication device capable of relaying neural activity from the motor cortex of paralyzed patients. The platform has been used to recover speech.
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24
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Bergeron D, Iorio-Morin C, Bonizzato M, Lajoie G, Orr Gaucher N, Racine É, Weil AG. Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations. J Child Neurol 2023; 38:223-238. [PMID: 37116888 PMCID: PMC10226009 DOI: 10.1177/08830738231167736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/11/2023] [Accepted: 03/17/2023] [Indexed: 04/30/2023]
Abstract
Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.
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Affiliation(s)
- David Bergeron
- Division of Neurosurgery, Université de Montréal, Montreal, Québec, Canada
| | | | - Marco Bonizzato
- Electrical Engineering Department, Polytechnique Montréal, Montreal, Québec, Canada
- Neuroscience Department and Centre
interdisciplinaire de recherche sur le cerveau et l’apprentissage (CIRCA), Université de Montréal, Montréal, Québec, Canada
| | - Guillaume Lajoie
- Mathematics and Statistics Department, Université de Montréal, Montreal, Québec, Canada
- Mila - Québec AI Institute, Montréal,
Québec, Canada
| | - Nathalie Orr Gaucher
- Department of Pediatric Emergency
Medicine, CHU Sainte-Justine, Montréal, Québec, Canada
- Bureau de l’Éthique clinique, Faculté
de médecine de l’Université de Montréal, Montreal, Québec, Canada
| | - Éric Racine
- Pragmatic Research Unit, Institute de
Recherche Clinique de Montréal (IRCM), Montreal, Québec, Canada
- Department of Medicine and Department
of Social and Preventative Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Alexander G. Weil
- Division of Neurosurgery, Department
of Surgery, Centre Hospitalier Universitaire Sainte-Justine (CHUSJ), Département de
Pédiatrie, Université de Montréal, Montreal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
- Brain and Development Research Axis,
CHU Sainte-Justine Research Center, Montréal, Québec, Canada
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25
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Resonance Scanning as an Efficiency Enhancer for EEG-Guided Adaptive Neurostimulation. Life (Basel) 2023; 13:life13030620. [PMID: 36983776 PMCID: PMC10056921 DOI: 10.3390/life13030620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Electroencephalogram (EEG)-guided adaptive neurostimulation is an innovative kind of non-invasive closed-loop brain stimulation technique that uses audio–visual stimulation on-line modulated by rhythmical EEG components of the individual. However, the opportunity to enhance its effectiveness is a challenging task and needs further investigation. The present study aims to experimentally test whether it is possible to increase the efficiency of EEG-guided adaptive neurostimulation by pre- strengthening the modulating factor (subject’s EEG) through the procedure of resonance scanning, i.e., LED photostimulation with the frequency gradually increasing in the range of main EEG rhythms (4–20 Hz). Thirty-six university students in a state of exam stress were randomly assigned to two matched groups. One group was presented with the EEG-guided adaptive neurostimulation alone, whereas another matched group was presented with the combination of resonance scanning and EEG-guided adaptive neurostimulation. The changes in psychophysiological indicators after stimulation relative to the initial level were used. Although both types of stimulation led to an increase in the power of EEG rhythms, accompanied by a decrease in the number of errors in the word recognition test and a decrease in the degree of emotional maladjustment, these changes reached the level of significance only in experiments with preliminary resonance scanning. Resonance scanning increases the brain’s responsiveness to subsequent EEG-guided adaptive neurostimulation, acting as a tool to enhance its efficiency. The results obtained clearly indicate that the combination of resonance scanning and EEG-guided adaptive neurostimulation is an effective way to reach the signs of cognitive improvement in stressed individuals.
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26
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Karwowski W, Soekadar SR, Kawala-Sterniuk A. Editorial: Brain imaging relations through simultaneous recordings. Front Neurosci 2023; 17:1139336. [PMID: 36824216 PMCID: PMC9941736 DOI: 10.3389/fnins.2023.1139336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Affiliation(s)
- Waldemar Karwowski
- Department of Industrial and Systems Engineering, University of Central Florida, Orlando, FL, United States,*Correspondence: Waldemar Karwowski ✉
| | - Surjo R. Soekadar
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité–Universitätsmedizin Berlin, Berlin, Germany,Surjo R. Soekadar ✉
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland,Aleksandra Kawala-Sterniuk ✉
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27
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Identifying Thematics in a Brain-Computer Interface Research. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:2793211. [PMID: 36643889 PMCID: PMC9833923 DOI: 10.1155/2023/2793211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 01/05/2023]
Abstract
This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.
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28
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Bauer W, Dylag KA, Lysiak A, Wieczorek-Stawinska W, Pelc M, Szmajda M, Martinek R, Zygarlicki J, Bańdo B, Stomal-Slowinska M, Kawala-Sterniuk A. Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy. Sci Rep 2023; 13:109. [PMID: 36596841 PMCID: PMC9810692 DOI: 10.1038/s41598-022-26590-4] [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: 04/20/2022] [Accepted: 12/16/2022] [Indexed: 01/04/2023] Open
Abstract
Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects (including central nervous system defects). Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things-the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics.
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Affiliation(s)
- Waldemar Bauer
- grid.9922.00000 0000 9174 1488Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
| | - Katarzyna Anna Dylag
- St. Louis Children Hospital in Krakow, 30-663 Kraków, Poland ,grid.5522.00000 0001 2162 9631Department of Pathophysiology, Jagiellonian University in Krakow – Collegium Medicum, 31-121 Kraków, Poland
| | - Adam Lysiak
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | | | - Mariusz Pelc
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland ,grid.36316.310000 0001 0806 5472School of Computing and Mathematical Sciences, University of Greenwich, London, SE10 9LS UK
| | - Miroslaw Szmajda
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Radek Martinek
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland ,grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, VSB—Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic
| | - Jaroslaw Zygarlicki
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Bożena Bańdo
- St. Louis Children Hospital in Krakow, 30-663 Kraków, Poland
| | | | - Aleksandra Kawala-Sterniuk
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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Ma Y, Gong A, Nan W, Ding P, Wang F, Fu Y. Personalized Brain-Computer Interface and Its Applications. J Pers Med 2022; 13:46. [PMID: 36675707 PMCID: PMC9861730 DOI: 10.3390/jpm13010046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.
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Affiliation(s)
- Yixin Ma
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xian 710086, China
| | - Wenya Nan
- Department of Psychology, College of Education, Shanghai Normal University, Shanghai 200234, China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China
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30
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de Bougrenet de la Tocnaye JL. Restored vision-augmented vision: arguments for a cybernetic vision. C R Biol 2022; 345:135-156. [PMID: 36847468 DOI: 10.5802/crbiol.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 11/17/2022] [Indexed: 12/14/2022]
Abstract
In this paper, we present some thoughts about the recent developments, made possible by technological advances and miniaturisation of connected visual prostheses, linked to the visual system, operating at different level of this one, on the retina as well as in the visual cortex. While these objects represent a great hope for people with impaired vision to recover partial vision, we show how this technology could also act on the functional vision of well sighted persons to improve or increase their visual performance. In addition to the impact on our cognitive and attentional mechanisms, such an operation when it originates outside the natural real visual field (e.g. cybernetics) raises a number of questions about the development and use of such implants or prostheses in the future.
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31
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Metzger SL, Liu JR, Moses DA, Dougherty ME, Seaton MP, Littlejohn KT, Chartier J, Anumanchipalli GK, Tu-Chan A, Ganguly K, Chang EF. Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis. Nat Commun 2022; 13:6510. [PMID: 36347863 PMCID: PMC9643551 DOI: 10.1038/s41467-022-33611-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 09/26/2022] [Indexed: 11/09/2022] Open
Abstract
Neuroprostheses have the potential to restore communication to people who cannot speak or type due to paralysis. However, it is unclear if silent attempts to speak can be used to control a communication neuroprosthesis. Here, we translated direct cortical signals in a clinical-trial participant (ClinicalTrials.gov; NCT03698149) with severe limb and vocal-tract paralysis into single letters to spell out full sentences in real time. We used deep-learning and language-modeling techniques to decode letter sequences as the participant attempted to silently spell using code words that represented the 26 English letters (e.g. "alpha" for "a"). We leveraged broad electrode coverage beyond speech-motor cortex to include supplemental control signals from hand cortex and complementary information from low- and high-frequency signal components to improve decoding accuracy. We decoded sentences using words from a 1,152-word vocabulary at a median character error rate of 6.13% and speed of 29.4 characters per minute. In offline simulations, we showed that our approach generalized to large vocabularies containing over 9,000 words (median character error rate of 8.23%). These results illustrate the clinical viability of a silently controlled speech neuroprosthesis to generate sentences from a large vocabulary through a spelling-based approach, complementing previous demonstrations of direct full-word decoding.
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Affiliation(s)
- Sean L. Metzger
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA ,grid.47840.3f0000 0001 2181 7878University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA USA
| | - Jessie R. Liu
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA ,grid.47840.3f0000 0001 2181 7878University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA USA
| | - David A. Moses
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA
| | - Maximilian E. Dougherty
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA
| | - Margaret P. Seaton
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA
| | - Kaylo T. Littlejohn
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA ,grid.47840.3f0000 0001 2181 7878Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA USA
| | - Josh Chartier
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA
| | - Gopala K. Anumanchipalli
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA ,grid.47840.3f0000 0001 2181 7878Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA USA
| | - Adelyn Tu-Chan
- grid.266102.10000 0001 2297 6811Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Karunesh Ganguly
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Department of Neurology, University of California, San Francisco, San Francisco, CA USA
| | - Edward F. Chang
- grid.266102.10000 0001 2297 6811Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA USA ,grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA USA ,grid.47840.3f0000 0001 2181 7878University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA USA
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OGANDO-RIVAS E, CASTILLO P, BELTRAN JQ, ARELLANO R, GALVAN-REMIGIO I, SOTO-ULLOA V, DIAZ-PEREGRINO R, OCHOA-HERNANDEZ D, REYES-GONZÁLEZ P, SAYOUR E, MITCHELL D. Evolution and Revolution of Imaging Technologies in Neurosurgery. Neurol Med Chir (Tokyo) 2022; 62:542-551. [PMID: 36288973 PMCID: PMC9831622 DOI: 10.2176/jns-nmc.2022-0116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
We understand only a small fraction of the events happening in our brains; therefore, despite all the progress made thus far, a whole array of questions remains. Nonetheless, neurosurgeons invented new tools to circumvent the challenges that had plagued their predecessors. With the manufacturing boom of the 20th century, technological innovations blossomed enabling the neuroscientific community to study and operate upon the living brain in finer detail and with greater precision while avoiding harm to the nervous system. The purpose of this chronological review is to 1) raise awareness among future neurosurgeons about the latest advances in the field, 2) become familiar with innovations such as augmented reality (AR) that should be included in education given their ready applicability in surgical training, and 3) be comfortable with customizing these technologies to real-life cases like in the case of mixed reality.
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Affiliation(s)
- Elizabeth OGANDO-RIVAS
- Department of Neurosurgery, Brain Tumor Immunotherapy Program, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Paul CASTILLO
- Department of Pediatrics, UF Health Shands Children's Hospital, Gainesville, FL, USA
| | - Jesus Q. BELTRAN
- Unit of Stereotactic and Functional Neurosurgery, General Hospital of Mexico, Mexico City, Mexico
| | - Rodolfo ARELLANO
- Department of Neurosurgery, CostaMed Medical Group, Quintana Roo, Mexico
| | | | - Victor SOTO-ULLOA
- Emergency Department, Hospital General #48, Instituto Mexicano del Seguro Social, Mexico City, México
| | | | | | | | - Elias SAYOUR
- Department of Neurosurgery, Brain Tumor Immunotherapy Program, McKnight Brain Institute, University of Florida, Gainesville, FL, USA,Department of Pediatrics, UF Health Shands Children's Hospital, Gainesville, FL, USA
| | - Duane MITCHELL
- Department of Neurosurgery, Brain Tumor Immunotherapy Program, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
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Al-Bakri AF, Martinek R, Pelc M, Zygarlicki J, Kawala-Sterniuk A. Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples. SENSORS (BASEL, SWITZERLAND) 2022; 22:7522. [PMID: 36236621 PMCID: PMC9571066 DOI: 10.3390/s22197522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/20/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.
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Affiliation(s)
- Amir F. Al-Bakri
- Department of Biomedical Engineering, College of Engineering, University of Babylon, Hillah 51001, Iraq
| | - Radek Martinek
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava–Poruba, Czech Republic
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- School of Computing and Mathematical Sciences, University of Greenwich, Park Row, London SE10 9LS, UK
| | - Jarosław Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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34
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de Bougrenet de la Tocnaye JL, Nourrit V, Lahuec C. Design of a Multimodal Oculometric Sensor Contact Lens. SENSORS (BASEL, SWITZERLAND) 2022; 22:6731. [PMID: 36146080 PMCID: PMC9504896 DOI: 10.3390/s22186731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/24/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
Oculometric data, such as gaze direction, pupil size and accommodative change, play a key role nowadays in the analysis of cognitive load and attentional activities, in particular with the development of Integrated Visual Augmentation Systems in many application domains, such as health, defense and industry. Such measurements are most frequently obtained by different devices, most of them requiring steady eye and body positions and controlled lighting conditions. Recent advances in smart contact lens (SCL) technology have demonstrated the ability to achieve highly reliable and accurate measurements, preserving user mobility, for instance in measuring gaze direction. In this paper, we discuss how these three key functions can be implemented and combined in the same SCL, considering the limited volume and energy consumption constraints. Some technical options are discussed and compared in terms of their ability to be implemented, taking advantage of recent developments in the field.
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Affiliation(s)
| | - Vincent Nourrit
- Optics Department, Institut Mines-Télécom Atlantique, Technopôle Brest Iroise, CS 83818, CEDEX 03, 29238 Brest, Brittany, France
| | - Cyril Lahuec
- Optics Department, Institut Mines-Télécom Atlantique, Technopôle Brest Iroise, CS 83818, CEDEX 03, 29238 Brest, Brittany, France
- Laboratoire des Sciences et Techniques de l’Information, de la Communication et de la Connaissance, CNRS UMR 6285, 29238 Brest, Brittany, France
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35
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Gannouni S, Belwafi K, Al-Sulmi MR, Al-Farhood MD, Al-Obaid OA, Al-Awadh AM, Aboalsamh H, Belghith A. A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer. Brain Sci 2022; 12:brainsci12070926. [PMID: 35884732 PMCID: PMC9313199 DOI: 10.3390/brainsci12070926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 12/04/2022] Open
Abstract
There are many applications controlled by the brain signals to bridge the gap in the digital divide between the disabled and the non-disabled people. The deployment of novel assistive technologies using brain-computer interface (BCI) will go a long way toward achieving this lofty goal, especially after the successes demonstrated by these technologies in the daily life of people with severe disabilities. This paper contributes in this direction by proposing an integrated framework to control the operating system functionalities using Electroencephalography signals. Different signal processing algorithms were applied to remove artifacts, extract features, and classify trials. The proposed approach includes different classification algorithms dedicated to detecting the P300 responses efficiently. The predicted commands passed through a socket to the API system, permitting the control of the operating system functionalities. The proposed system outperformed those obtained by the winners of the BCI competition and reached an accuracy average of 94.5% according to the offline approach. The framework was evaluated according to the online process and achieved an excellent accuracy attaining 97% for some users but not less than 90% for others. The suggested framework enhances the information accessibility for people with severe disabilities and helps them perform their daily tasks efficiently. It permits the interaction between the user and personal computers through the brain signals without any muscular efforts.
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Affiliation(s)
- Sofien Gannouni
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
| | - Kais Belwafi
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
- Electrical Engineering and Computer Science department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Correspondence: or
| | - Mohammad Reshood Al-Sulmi
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
| | - Meshal Dawood Al-Farhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
| | - Omar Ali Al-Obaid
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
| | - Abdullah Mohammed Al-Awadh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
| | - Hatim Aboalsamh
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
| | - Abdelfettah Belghith
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (S.G.); (M.R.A.-S.); (M.D.A.-F.); (O.A.A.-O.); (A.M.A.-A.); (H.A.); (A.B.)
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36
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Phogat R, Parmananda P, Prasad A. Intensity dependence of sub-harmonics in cortical response to photic stimulation. J Neural Eng 2022; 19. [PMID: 35839731 DOI: 10.1088/1741-2552/ac817f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/15/2022] [Indexed: 11/11/2022]
Abstract
Objective. Periodic photic stimulation of human volunteers at 10 Hz is known to entrain their Electroencephalography (EEG) signals. This entrainment manifests as an increment in power at 10, 20, 30 Hz. We observed that this entrainment is accompanied by the emergence of sub-harmonics, but only at specific frequencies and higher intensities of the stimulating signal. Thereafter, we describe our results and explain them using the physiologically inspired Jansen and Rit Neural Mass Model (NMM).Approach. Four human volunteers were separately exposed to both high and low intensity 10 Hz and 6 Hz stimulation. A total of 4 experiments per subject were therefore performed. Simulations and bifurcation analysis of the NMM were carried out and compared with the experimental findings. <i> Main results. High intensity 10 Hz stimulation led to an increment in power at 5 Hz across all the 4 subjects. No increment of power was observed with low intensity stimulation. However, when the same protocol was repeated with a 6 Hz photic stimulation, neither high nor low intensity stimulation were found to cause a discernible change in power at 3 Hz. We found that the NMM was able to recapitulate these results. A further numerical analysis indicated that this arises from the underlying bifurcation structure of the NMM. <i> Significance. The excellent match between theory and experiment suggest that the bifurcation properties of the NMM are mirroring similar features possessed by the actual neural masses producing the EEG dynamics. Neural Mass Models could thus be valuable for understanding properties and pathologies of EEG dynamics, and may contribute to the engineering of brain-computer interface technologies.
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Affiliation(s)
- Richa Phogat
- Indian Institute of Technology Bombay, Department of Physics, Indian Institute of Technology - Bombay, Mumbai, 400076, INDIA
| | - P Parmananda
- Indian Institute of Technology Bombay, Department of Physics, Indian Institute of Technology - Bombay, Mumbai, Maharashtra, 400076, INDIA
| | - Ashok Prasad
- Colorado State University, Department of Chemical and Biological Engineering and School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, 80523-1019, UNITED STATES
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Chi X, Wan C, Wang C, Zhang Y, Chen X, Cui H. A Novel Hybrid Brain-Computer Interface Combining Motor Imagery and Intermodulation Steady-State Visual Evoked Potential. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1525-1535. [PMID: 35657833 DOI: 10.1109/tnsre.2022.3179971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The hybrid brain-computer interface (hBCI) combining motor imagery (MI) and steady-state visual evoked potential (SSVEP) has been proven to have better performance than a pure MI- or SSVEP-based brain-computer interface (BCI). In most studies on hBCIs, subjects have been required to focus their attention on flickering light-emitting diodes (LEDs) or blocks while imagining body movements. However, these two classical tasks performed concurrently have a poor correlation. Therefore, it is necessary to reduce the task complexity of such a system and improve its user-friendliness. Aiming to achieve this goal, this study proposes a novel hybrid BCI that combines MI and intermodulation SSVEPs. In the proposed system, images of both hands flicker at the same frequency (i.e., 30 Hz) but at different grasp frequencies (i.e., 1 Hz for the left hand, and 1.5 Hz for the right hand), resulting in different intermodulation frequencies for encoding targets. Additionally, movement observation for subjects can help to perform the MI task better. In this study, two types of brain signals are classified independently and then fused by a scoring mechanism based on the probability distribution of relevant parameters. The online verification results showed that the average accuracies of 12 healthy subjects and 11 stroke patients were 92.40 ± 7.45% and 73.07 ± 9.07%, respectively. The average accuracies of 10 healthy subjects in the MI, SSVEP, and hybrid tasks were 84.00 ± 12.81%, 80.75 ± 8.08%, and 89.00 ± 9.94%, respectively. The high recognition accuracy verifies the feasibility and robustness of the proposed system. This study provides a novel and natural paradigm for a hybrid BCI based on MI and SSVEP.
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38
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Arpaia P, Esposito A, Natalizio A, Parvis M. How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art. J Neural Eng 2022; 19. [PMID: 35640554 DOI: 10.1088/1741-2552/ac74e0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 05/31/2022] [Indexed: 11/11/2022]
Abstract
Objective. Processing strategies are analysed with respect to the classification of electroencephalographic signals related to brain-computer interfaces based on motor imagery. A review of literature is carried out to understand the achievements in motor imagery classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility.Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery- based brain-computer interfaces. Article search was carried out in accordance with the PRISMA standard and 89 studies were included.Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85 % to 100 % range for the binary case and in the 83 % to 93 % range for multi-class one. Associated uncertainties are up to 6 % while repeatability for a predetermined dataset is up to 8 %. Reproducibility assessment was instead prevented by lack of standardization in experiments.Significance. By relying on the analysed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a brain-computer interface. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of results reproducibility.
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Affiliation(s)
- Pasquale Arpaia
- Centro Interdipartimentale di Ricerca in Management Sanitario e Innovazione in Sanità, Università degli Studi di Napoli Federico II, Via Claudio, 21, Napoli, Campania, 80125, ITALY
| | - Antonio Esposito
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, Corso Castelfidardo, 39, Torino, 10129, ITALY
| | - Angela Natalizio
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, Corso Castelfidardo, 39, Torino, Piemonte, 10129, ITALY
| | - Marco Parvis
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, Corso Castelfidardo, 39, Torino, Piemonte, 10129, ITALY
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39
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Ye H, Li G, Sheng X, Zhu X. Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task. J Neural Eng 2022; 19. [PMID: 35441594 DOI: 10.1088/1741-2552/ac63e9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/04/2022] [Indexed: 11/12/2022]
Abstract
Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG.Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads.Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16-3.84 Hz for the phase and 50-85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load.Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.
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Affiliation(s)
- Huanpeng Ye
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Guangye Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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40
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Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9935192. [PMID: 35252458 PMCID: PMC8896931 DOI: 10.1155/2022/9935192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Brain-computer interfaces (BCIs), a new type of rehabilitation technology, pick up nerve cell signals, identify and classify their activities, and convert them into computer-recognized instructions. This technique has been widely used in the rehabilitation of stroke patients in recent years and appears to promote motor function recovery after stroke. At present, the application of BCI in poststroke cognitive impairment is increasing, which is a common complication that also affects the rehabilitation process. This paper reviews the promise and potential drawbacks of using BCI to treat poststroke cognitive impairment, providing a solid theoretical basis for the application of BCI in this area.
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Gonzalez-Navarro P, Celik B, Moghadamfalahi M, Akcakaya M, Fried-Oken M, Erdoğmuş D. Feedback Related Potentials for EEG-Based Typing Systems. Front Hum Neurosci 2022; 15:788258. [PMID: 35145386 PMCID: PMC8821166 DOI: 10.3389/fnhum.2021.788258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Error related potentials (ErrP), which are elicited in the EEG in response to a perceived error, have been used for error correction and adaption in the event related potential (ERP)-based brain computer interfaces designed for typing. In these typing interfaces, ERP evidence is collected in response to a sequence of stimuli presented usually in the visual form and the intended user stimulus is probabilistically inferred (stimulus with highest probability) and presented to the user as the decision. If the inferred stimulus is incorrect, ErrP is expected to be elicited in the EEG. Early approaches to use ErrP in the design of typing interfaces attempt to make hard decisions on the perceived error such that the perceived error is corrected and either the sequence of stimuli are repeated to obtain further ERP evidence, or without further repetition the stimulus with the second highest probability is presented to the user as the decision of the system. Moreover, none of the existing approaches use a language model to increase the performance of typing. In this work, unlike the existing approaches, we study the potential benefits of fusing feedback related potentials (FRP), a form of ErrP, with ERP and context information (language model, LM) in a Bayesian fashion to detect the user intent. We present experimental results based on data from 12 healthy participants using RSVP Keyboard™ to complete a copy-phrase-task. Three paradigms are compared: [P1] uses only ERP/LM Bayesian fusion; [P2] each RSVP sequence is appended with the top candidate in the alphabet according to posterior after ERP evidence fusion; corresponding FRP is then incorporated; and [P3] the top candidate is shown as a prospect to generate FRP evidence only if its posterior exceeds a threshold. Analyses indicate that ERP/LM/FRP evidence fusion during decision making yields significant speed-accuracy benefits for the user.
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Affiliation(s)
- Paula Gonzalez-Navarro
- Cognitive Systems Laboratory, Northeastern University, Boston, MA, United States
- *Correspondence: Paula Gonzalez-Navarro
| | - Basak Celik
- Cognitive Systems Laboratory, Northeastern University, Boston, MA, United States
- CAMBI (Consortium for Accessible Multimodal Brain-Body Interfaces), Portland, OR, United States
- Basak Celik
| | | | - Murat Akcakaya
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PI, United States
| | - Melanie Fried-Oken
- CAMBI (Consortium for Accessible Multimodal Brain-Body Interfaces), Portland, OR, United States
- Institute on Development and Disability, Oregon Health & Science University, Portland, OR, United States
| | - Deniz Erdoğmuş
- Cognitive Systems Laboratory, Northeastern University, Boston, MA, United States
- CAMBI (Consortium for Accessible Multimodal Brain-Body Interfaces), Portland, OR, United States
- Deniz Erdoğmuş
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42
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CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain. SENSORS 2021; 21:s21227605. [PMID: 34833681 PMCID: PMC8617901 DOI: 10.3390/s21227605] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/09/2021] [Accepted: 11/11/2021] [Indexed: 11/16/2022]
Abstract
The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of eye movements by a camera offers a particularly attractive external sensor for computer interfaces to monitor, assess, and control these higher brain functions without acquiring signals from the brain. Features of gaze position and pupil dilation can be effectively used to track our attention in healthy mental processes, to enable interaction in disorders of consciousness, or to even predict memory performance in various brain diseases. In this perspective article, we propose the term ‘CyberEye’ to encompass emerging cognitive applications of eye-tracking interfaces for neuroscience research, clinical practice, and the biomedical industry. As CyberEye technologies continue to develop, we expect BCIs to become less dependent on brain activities, to be less invasive, and to thus be more applicable.
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6343. [PMID: 34640663 PMCID: PMC8512967 DOI: 10.3390/s21196343] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022]
Abstract
As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
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Affiliation(s)
- Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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Salahuddin U, Gao PX. Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review. Front Neurosci 2021; 15:728178. [PMID: 34588951 PMCID: PMC8475516 DOI: 10.3389/fnins.2021.728178] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 08/18/2021] [Indexed: 11/13/2022] Open
Abstract
Brain machine interfaces (BMIs), or brain computer interfaces (BCIs), are devices that act as a medium for communications between the brain and the computer. It is an emerging field with numerous applications in domains of prosthetic devices, robotics, communication technology, gaming, education, and security. It is noted in such a multidisciplinary field, many reviews have surveyed on various focused subfields of interest, such as neural signaling, microelectrode fabrication, and signal classification algorithms. A unified review is lacking to cover and link all the relevant areas in this field. Herein, this review intends to connect on the relevant areas that circumscribe BMIs to present a unified script that may help enhance our understanding of BMIs. Specifically, this article discusses signal generation within the cortex, signal acquisition using invasive, non-invasive, or hybrid techniques, and the signal processing domain. The latest development is surveyed in this field, particularly in the last decade, with discussions regarding the challenges and possible solutions to allow swift disruption of BMI products in the commercial market.
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Affiliation(s)
- Usman Salahuddin
- Institute of Materials Science, University of Connecticut, Storrs, CT, United States
| | - Pu-Xian Gao
- Institute of Materials Science, University of Connecticut, Storrs, CT, United States
- Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, United States
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45
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Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach. SENSORS 2021; 21:s21165308. [PMID: 34450750 PMCID: PMC8439358 DOI: 10.3390/s21165308] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/31/2021] [Accepted: 08/01/2021] [Indexed: 01/16/2023]
Abstract
This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches-the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey's test.
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Belwafi K, Gannouni S, Aboalsamh H. Embedded Brain Computer Interface: State-of-the-Art in Research. SENSORS 2021; 21:s21134293. [PMID: 34201788 PMCID: PMC8271671 DOI: 10.3390/s21134293] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 12/02/2022]
Abstract
There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.
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Identification of Brain Electrical Activity Related to Head Yaw Rotations. SENSORS 2021; 21:s21103345. [PMID: 34065035 PMCID: PMC8150891 DOI: 10.3390/s21103345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/04/2021] [Accepted: 05/07/2021] [Indexed: 12/20/2022]
Abstract
Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.
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48
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Browarska N, Kawala-Sterniuk A, Zygarlicki J, Podpora M, Pelc M, Martinek R, Gorzelańczyk EJ. Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation. Brain Sci 2021; 11:98. [PMID: 33451080 PMCID: PMC7828570 DOI: 10.3390/brainsci11010098] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/02/2021] [Accepted: 01/08/2021] [Indexed: 12/15/2022] Open
Abstract
Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky-Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.
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Affiliation(s)
- Natalia Browarska
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Jaroslaw Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Michal Podpora
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
- Department of Computing and Information Systems, University of Greenwich, London SE10 9LS, UK
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, FEECS, VSB-Technical University Ostrava, 708 00 Ostrava-Poruba, Czech Republic;
| | - Edward Jacek Gorzelańczyk
- Department of Theoretical Basis of BioMedical Sciences and Medical Informatics, Nicolaus Copernicus University, Collegium Medicum, 85-067 Bydgoszcz, Poland;
- Institute of Philosophy, Kazimierz Wielki University, 85-092 Bydgoszcz, Poland
- Outpatient Addiction Treatment, Babinski Specialist Psychiatric Healthcare Center, 91-229 Lodz, Poland
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