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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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Huo C, Xu G, Xie H, Chen T, Shao G, Wang J, Li W, Wang D, Li Z. Functional near-infrared spectroscopy in non-invasive neuromodulation. Neural Regen Res 2024; 19:1517-1522. [PMID: 38051894 PMCID: PMC10883499 DOI: 10.4103/1673-5374.387970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/14/2023] [Indexed: 12/07/2023] Open
Abstract
ABSTRACT Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks, which have been widely applied in the field of central neurological diseases, such as stroke, Parkinson's disease, and mental disorders. Although significant advances have been made in neuromodulation technologies, the identification of optimal neurostimulation parameters including the cortical target, duration, and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits. Moreover, the neural mechanism underlying neuromodulation for improved behavioral performance remains poorly understood. Recently, advancements in neuroimaging have provided insight into neuromodulation techniques. Functional near-infrared spectroscopy, as a novel non-invasive optical brain imaging method, can detect brain activity by measuring cerebral hemodynamics with the advantages of portability, high motion tolerance, and anti-electromagnetic interference. Coupling functional near-infrared spectroscopy with neuromodulation technologies offers an opportunity to monitor the cortical response, provide real-time feedback, and establish a closed-loop strategy integrating evaluation, feedback, and intervention for neurostimulation, which provides a theoretical basis for development of individualized precise neurorehabilitation. We aimed to summarize the advantages of functional near-infrared spectroscopy and provide an overview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation, transcranial electrical stimulation, neurofeedback, and brain-computer interfaces. Furthermore, the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized. In conclusion, functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central neural reorganization to achieve better functional recovery from central nervous system diseases.
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Affiliation(s)
- Congcong Huo
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Gongcheng Xu
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Hui Xie
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Tiandi Chen
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
| | - Guangjian Shao
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, Guangdong Province, China
| | - Jue Wang
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing, China
| | - Wenhao Li
- School of Rehabilitation Engineering, Beijing College of Social Administration, Beijing, China
| | - Daifa Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing, China
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Rao Y, Zhang L, Jing R, Huo J, Yan K, He J, Hou X, Mu J, Geng W, Cui H, Hao Z, Zan X, Ma J, Chou X. An optimized EEGNet decoder for decoding motor image of four class fingers flexion. Brain Res 2024; 1841:149085. [PMID: 38876320 DOI: 10.1016/j.brainres.2024.149085] [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: 01/29/2024] [Revised: 05/23/2024] [Accepted: 06/10/2024] [Indexed: 06/16/2024]
Abstract
As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.
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Affiliation(s)
- Yongkang Rao
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Le Zhang
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Ruijun Jing
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Jiabing Huo
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Kunxian Yan
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Jian He
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Xiaojuan Hou
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Jiliang Mu
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Wenping Geng
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Haoran Cui
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
| | - Zeyu Hao
- Science and Technology on Electronic Test & Measurement Laboratory, The 41st Institute of China Electronic Technology Group Corporation, Qingdao 266555, China
| | - Xiang Zan
- Shanxi Provincial People's Hospital, the Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, China
| | - Jiuhong Ma
- Shanxi Provincial People's Hospital, the Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, China
| | - Xiujian Chou
- Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan 030051, China
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4
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Luo S, Meng Q, Li S, Yu H. Research of intent recognition in rehabilitation robots: a systematic review. Disabil Rehabil Assist Technol 2024; 19:1307-1318. [PMID: 36695473 DOI: 10.1080/17483107.2023.2170477] [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: 03/21/2022] [Revised: 01/10/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023]
Abstract
PURPOSE Rehabilitation robots with intent recognition are helping people with dysfunction to enjoy better lives. Many rehabilitation robots with intent recognition have been developed by academic institutions and commercial companies. However, there is no systematic summary about the application of intent recognition in the field of rehabilitation robots. Therefore, the purpose of this paper is to summarize the application of intent recognition in rehabilitation robots, analyze the current status of their research, and provide cutting-edge research directions for colleagues. MATERIALS AND METHODS Literature searches were conducted on Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Medline. Search terms included "rehabilitation robot", "intent recognition", "exoskeleton", "prosthesis", "surface electromyography (sEMG)" and "electroencephalogram (EEG)". References listed in relevant literature were further screened according to inclusion and exclusion criteria. RESULTS In this field, most studies have recognized movement intent by kinematic, sEMG, and EEG signals. However, in practical studies, the development of intent recognition in rehabilitation robots is limited by the hysteresis of kinematic signals and the weak anti-interference ability of sEMG and EEG signals. CONCLUSIONS Intent recognition has achieved a lot in the field of rehabilitation robotics but the key factors limiting its development are still timeliness and accuracy. In the future, intent recognition strategy with multi-sensor information fusion may be a good solution.
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Affiliation(s)
- Shengli Luo
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | | | - Sujiao Li
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
| | - Hongliu Yu
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
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Gangadharan SK, Ramakrishnan S, Paek A, Ravindran A, Prasad VA, Vidal JLC. Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation. Ann Indian Acad Neurol 2024; 27:297-306. [PMID: 38835164 PMCID: PMC11232817 DOI: 10.4103/aian.aian_1056_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 06/06/2024] Open
Abstract
OBJECTIVE Motor imagery-based brain-computer interface (MI-BCI) is a promising novel mode of stroke rehabilitation. The current study aims to investigate the feasibility of MI-BCI in upper limb rehabilitation of chronic stroke survivors and also to study the early event-related desynchronization after MI-BCI intervention. METHODS Changes in the characteristics of sensorimotor rhythm modulations in response to a short brain-computer interface (BCI) intervention for upper limb rehabilitation of stroke-disabled hand and normal hand were examined. The participants were trained to modulate their brain rhythms through motor imagery or execution during calibration, and they played a virtual marble game during the feedback session, where the movement of the marble was controlled by their sensorimotor rhythm. RESULTS Ipsilesional and contralesional activities were observed in the brain during the upper limb rehabilitation using BCI intervention. All the participants were able to successfully control the position of the virtual marble using their sensorimotor rhythm. CONCLUSIONS The preliminary results support the feasibility of BCI in upper limb rehabilitation and unveil the capability of MI-BCI as a promising medical intervention. This study provides a strong platform for clinicians to build upon new strategies for stroke rehabilitation by integrating MI-BCI with various therapeutic options to induce neural plasticity and recovery.
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Affiliation(s)
- Sagila K Gangadharan
- Department of Electrical Engineering, Indian Institute of Technology Palakkad, Palakkad, Kerala, India
| | - Subasree Ramakrishnan
- Department of Neurology, National Institute of Mental Health and Neuroscience, Bengaluru, Karnataka, India
| | - Andrew Paek
- Department of Electrical and Computer Engineering, Noninvasive Brain Machine Interface Systems Lab, University of Houston, Houston, USA
| | - Akshay Ravindran
- Department of Electrical and Computer Engineering, Noninvasive Brain Machine Interface Systems Lab, University of Houston, Houston, USA
| | - Vinod A Prasad
- Infocomm Technology Cluster, Singapore Institute of Technology, Singapore
| | - Jose L Contreras Vidal
- Department of Electrical and Computer Engineering, Noninvasive Brain Machine Interface Systems Lab, University of Houston, Houston, USA
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6
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Li X, Li H, Liu Y, Liang W, Zhang L, Zhou F, Zhang Z, Yuan X. The effect of electromyographic feedback functional electrical stimulation on the plantar pressure in stroke patients with foot drop. Front Neurosci 2024; 18:1377702. [PMID: 38629052 PMCID: PMC11018889 DOI: 10.3389/fnins.2024.1377702] [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: 01/28/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024] Open
Abstract
Purpose The purpose of this study was to observe, using Footscan analysis, the effect of electromyographic feedback functional electrical stimulation (FES) on the changes in the plantar pressure of drop foot patients. Methods This case-control study enrolled 34 stroke patients with foot drop. There were 17 cases received FES for 20 min per day, 5 days per week for 4 weeks (the FES group) and the other 17 cases only received basic rehabilitations (the control group). Before and after 4 weeks, the walking speed, spatiotemporal parameters and plantar pressure were measured. Results After 4 weeks treatments, Both the FES and control groups had increased walking speed and single stance phase percentage, decreased step length symmetry index (SI), double stance phase percentage and start time of the heel after 4 weeks (p < 0.05). The increase in walking speed and decrease in step length SI in the FES group were more significant than the control group after 4 weeks (p < 0.05). The FES group had an increased initial contact phase, decreased SI of the maximal force (Max F) and impulse in the medial heel after 4 weeks (p < 0.05). Conclusion The advantages of FES were: the improvement of gait speed, step length SI, and the enhancement of propulsion force were more significant. The initial contact phase was closer to the normal range, which implies that the control of ankle dorsiflexion was improved. The plantar dynamic parameters between the two sides of the foot were more balanced than the control group. FES is more effective than basic rehabilitations for stroke patients with foot drop based on current spatiotemporal parameters and plantar pressure results.
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Affiliation(s)
| | | | | | | | | | | | - Zhiqiang Zhang
- Department of Rehabilitation, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiangnan Yuan
- Department of Rehabilitation, Shengjing Hospital of China Medical University, Shenyang, China
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Ogihara H, Yamamoto N, Kurasawa Y, Kamo T, Hagiyama A, Hayashi S, Momosaki R. Characteristics and Methodological Quality of the Top 50 Most Influential Articles on Stroke Rehabilitation: A Bibliometric Analysis. Am J Phys Med Rehabil 2024; 103:363-369. [PMID: 38207163 DOI: 10.1097/phm.0000000000002412] [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: 01/13/2024]
Abstract
ABSTRACT This study aimed to conduct a comprehensive review of the top 50 most influential articles on stroke rehabilitation to investigate characteristics, such as the number of citations, year of publication, study design, and research topic, as well as to assess the evidence level and methodological quality. Moreover, we performed a supplementary assessment of the top 10 articles published within the past 5 yrs in the same domain, aiming to discern potential shifts in trends and methodological quality. Web of Science was used to search for articles on stroke rehabilitation. The data extracted from the articles included title, journal impact factor, year of publication, total number of citations, article topic, study design, and others. The level of evidence and methodological quality were assessed by two reviewers. Noninvasive brain stimulation and robotic rehabilitation were frequently discussed in the top 50 articles. We found that there was no difference in methodology quality between the top 50 articles in all years and the top ten articles in the past 5 yrs. Furthermore, the number of citations and citation density were not associated with the methodological quality. The findings suggest that the number of citations alone may not be a reliable indicator of research quality.
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Affiliation(s)
- Hirofumi Ogihara
- From the Division of Physical Therapy, Department of Rehabilitation, Faculty of Health Sciences, Nagano University of Health and Medicine, Nagano, Japan (HO, YK); Scientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan (HO, NY, YK, TK, AH, SH, RM); Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan (NY, AH); Department of Physical Therapy, Faculty of Rehabilitation, Gunma Paz University, Gunma, Japan (TK, SH); Division of Physical Medicine and Rehabilitation, Okayama University Hospital, Okayama, Japan (AH); and Department of Rehabilitation Medicine, Mie University Graduate School of Medicine, Mie, Japan (RM)
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8
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Beles H, Vesselenyi T, Rus A, Mitran T, Scurt FB, Tolea BA. Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions. SENSORS (BASEL, SWITZERLAND) 2024; 24:1541. [PMID: 38475079 DOI: 10.3390/s24051541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands.
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Affiliation(s)
- Horia Beles
- Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania
| | - Tiberiu Vesselenyi
- Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania
| | - Alexandru Rus
- Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania
| | - Tudor Mitran
- Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania
| | - Florin Bogdan Scurt
- Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania
| | - Bogdan Adrian Tolea
- Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania
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Oh E, Shin S, Kim SP. Brain-computer interface in critical care and rehabilitation. Acute Crit Care 2024; 39:24-33. [PMID: 38224957 PMCID: PMC11002623 DOI: 10.4266/acc.2023.01382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/08/2023] [Indexed: 01/17/2024] Open
Abstract
This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.
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Affiliation(s)
- Eunseo Oh
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
| | - Seyoung Shin
- Department of Mechanical Engineering, Sungkyunkwan University, Suwon, Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Korea
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10
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Taghlabi KM, Cruz-Garza JG, Hassan T, Potnis O, Bhenderu LS, Guerrero JR, Whitehead RE, Wu Y, Luan L, Xie C, Robinson JT, Faraji AH. Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review. J Neural Eng 2024; 21:011001. [PMID: 38237175 DOI: 10.1088/1741-2552/ad200f] [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: 04/03/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024]
Abstract
Peripheral nerve interfaces (PNIs) are electrical systems designed to integrate with peripheral nerves in patients, such as following central nervous system (CNS) injuries to augment or replace CNS control and restore function. We review the literature for clinical trials and studies containing clinical outcome measures to explore the utility of human applications of PNIs. We discuss the various types of electrodes currently used for PNI systems and their functionalities and limitations. We discuss important design characteristics of PNI systems, including biocompatibility, resolution and specificity, efficacy, and longevity, to highlight their importance in the current and future development of PNIs. The clinical outcomes of PNI systems are also discussed. Finally, we review relevant PNI clinical trials that were conducted, up to the present date, to restore the sensory and motor function of upper or lower limbs in amputees, spinal cord injury patients, or intact individuals and describe their significant findings. This review highlights the current progress in the field of PNIs and serves as a foundation for future development and application of PNI systems.
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Affiliation(s)
- Khaled M Taghlabi
- Department of Neurological Surgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
- Center for Neural Systems Restoration, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Clinical Innovations Laboratory, Houston Methodist Research Institute, Houston, TX 77030, United States of America
| | - Jesus G Cruz-Garza
- Department of Neurological Surgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
- Center for Neural Systems Restoration, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Clinical Innovations Laboratory, Houston Methodist Research Institute, Houston, TX 77030, United States of America
| | - Taimur Hassan
- Department of Neurological Surgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
- Center for Neural Systems Restoration, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Clinical Innovations Laboratory, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- School of Medicine, Texas A&M University, Bryan, TX 77807, United States of America
| | - Ojas Potnis
- Department of Neurological Surgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
- Center for Neural Systems Restoration, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Clinical Innovations Laboratory, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- School of Engineering Medicine, Texas A&M University, Houston, TX 77030, United States of America
| | - Lokeshwar S Bhenderu
- Department of Neurological Surgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
- Center for Neural Systems Restoration, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Clinical Innovations Laboratory, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- School of Medicine, Texas A&M University, Bryan, TX 77807, United States of America
| | - Jaime R Guerrero
- Department of Neurological Surgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
- Center for Neural Systems Restoration, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Clinical Innovations Laboratory, Houston Methodist Research Institute, Houston, TX 77030, United States of America
| | - Rachael E Whitehead
- Department of Academic Affairs, Houston Methodist Academic Institute, Houston, TX 77030, United States of America
| | - Yu Wu
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, United States of America
| | - Lan Luan
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, United States of America
| | - Chong Xie
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, United States of America
| | - Jacob T Robinson
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, United States of America
| | - Amir H Faraji
- Department of Neurological Surgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
- Center for Neural Systems Restoration, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Clinical Innovations Laboratory, Houston Methodist Research Institute, Houston, TX 77030, United States of America
- Rice Neuroengineering Initiative, Rice University, Houston, TX 77005, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, United States of America
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11
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Corsi MC, Sorrentino P, Schwartz D, George N, Gollo LL, Chevallier S, Hugueville L, Kahn AE, Dupont S, Bassett DS, Jirsa V, De Vico Fallani F. Measuring neuronal avalanches to inform brain-computer interfaces. iScience 2024; 27:108734. [PMID: 38226174 PMCID: PMC10788504 DOI: 10.1016/j.isci.2023.108734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/18/2023] [Accepted: 12/12/2023] [Indexed: 01/17/2024] Open
Abstract
Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.
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Affiliation(s)
- Marie-Constance Corsi
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Inria, Aramis Team, Paris, France
| | - Pierpaolo Sorrentino
- Institut de Neuroscience des Systèmes, Aix-Marseille University, Inserm, Marseille, France
| | - Denis Schwartz
- Institut du Cerveau - Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, CENIR, Centre MEG-EEG, Paris, France
| | - Nathalie George
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Institut du Cerveau - Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, CENIR, Centre MEG-EEG, Paris, France
| | - Leonardo L. Gollo
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia
| | | | - Laurent Hugueville
- Institut de Neuroscience des Systèmes, Aix-Marseille University, Inserm, Marseille, France
| | - Ari E. Kahn
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Sophie Dupont
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | | | - Viktor Jirsa
- Institut de Neuroscience des Systèmes, Aix-Marseille University, Inserm, Marseille, France
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Inria, Aramis Team, Paris, France
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12
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Gurgone S, De Salvo S, Bonanno L, Muscarà N, Acri G, Caridi F, Paladini G, Borzelli D, Brigandì A, La Torre D, Sorbera C, Anfuso C, Di Lorenzo G, Venuti V, d'Avella A, Marino S. Changes in cerebral cortex activity during a simple motor task after MRgFUS treatment in patients affected by essential tremor and Parkinson's disease: a pilot study using functional NIRS. Phys Med Biol 2024; 69:025014. [PMID: 38100845 DOI: 10.1088/1361-6560/ad164e] [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: 05/17/2023] [Accepted: 12/15/2023] [Indexed: 12/17/2023]
Abstract
Objective.Magnetic resonance imaging-guided focused ultrasound surgery (MRgFUS) is a non-invasive thermal ablation method that involves high-intensity focused ultrasound surgery (FUS) and Magnetic Resonance Imaging for anatomical imaging and real-time thermal mapping. This technique is widely employed for the treatment of patients affected by essential tremor (ET) and Parkinson's disease (PD). In the current study, functional near-infrared spectroscopy (fNIRS) was used to highlight hemodynamics changes in cerebral cortex activity, during a simple hand motor task, i.e. unimanual left and right finger-tapping, in ET and PD patients.Approach.All patients were evaluated before, one week and one month after MRgFUS treatment.Main results.fNIRS revealed cerebral hemodynamic changes one week and one month after MRgFUS treatment, especially in the ET group, that showed a significant clinical improvement in tremor clinical scores.Significance.To our knowledge, our study is the first that showed the use of fNIRS system to measure the cortical activity changes following unilateral ventral intermediate nucleus thalamotomy after MRgFUS treatment. Our findings showed that therapeutic MRgFUS promoted the remodeling of neuronal networks and changes in cortical activity in association with symptomatic improvements.
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Affiliation(s)
- Sergio Gurgone
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, 1-4, Yamadaoka, Suita City, 565-0871 Osaka, Japan
| | - Simona De Salvo
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
| | - Lilla Bonanno
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
| | - Nunzio Muscarà
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
| | - Giuseppe Acri
- Dipartimento di Scienze Biomediche, Odontoiatriche, e delle Immagini Morfologiche e Funzionali, Università degli Studi di Messina, c/o A.O.U. Policlinico 'G. Martino' Via Consolare Valeria 1, I-98125 Messina, Italy
| | - Francesco Caridi
- Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università degli Studi di Messina, V.le F. Stagno D'Alcontres 31, I-98166 Messina, Italy
| | - Giuseppe Paladini
- Dipartimento di Fisica e Astronomia 'Ettore Majorana', Università degli Studi di Catania, Via S. Sofia 64, I-95123 Catania, Italy
| | - Daniele Borzelli
- Dipartimento di Scienze Biomediche, Odontoiatriche, e delle Immagini Morfologiche e Funzionali, Università degli Studi di Messina, c/o A.O.U. Policlinico 'G. Martino' Via Consolare Valeria 1, I-98125 Messina, Italy
- Laboratorio di Fisiologia Neuromotoria, IRCCS Fondazione Santa Lucia, Via Ardeatina 306-354, I-00179 Roma, Italy
| | - Amelia Brigandì
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
| | - Domenico La Torre
- Dipartimento di Scienze Mediche e Chirurgiche, Istituto di Neurochirurgia, Università degli Studi 'Magna Graecia' di Catanzaro, Viale Europa, I-88100 Catanzaro, Italy
| | - Chiara Sorbera
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
| | - Carmelo Anfuso
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
| | - Giuseppe Di Lorenzo
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
| | - Valentina Venuti
- Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università degli Studi di Messina, V.le F. Stagno D'Alcontres 31, I-98166 Messina, Italy
| | - Andrea d'Avella
- Dipartimento di Scienze Biomediche, Odontoiatriche, e delle Immagini Morfologiche e Funzionali, Università degli Studi di Messina, c/o A.O.U. Policlinico 'G. Martino' Via Consolare Valeria 1, I-98125 Messina, Italy
- Laboratorio di Fisiologia Neuromotoria, IRCCS Fondazione Santa Lucia, Via Ardeatina 306-354, I-00179 Roma, Italy
| | - Silvia Marino
- IRCCS Centro Neurolesi 'Bonino-Pulejo', Via Palermo, Ctr. Casazza, S.S. 113, I-98121 Messina, Italy
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13
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Zhu L, Xu M, Zhu J, Huang A, Zhang J. A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs. Comput Methods Biomech Biomed Engin 2024:1-14. [PMID: 38193151 DOI: 10.1080/10255842.2023.2301421] [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: 10/12/2023] [Accepted: 12/24/2023] [Indexed: 01/10/2024]
Abstract
Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial's data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.
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Affiliation(s)
- Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Mengxuan Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jieping Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Aiai Huang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
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14
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Ali O, Saif-Ur-Rehman M, Glasmachers T, Iossifidis I, Klaes C. ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data. Comput Biol Med 2024; 168:107649. [PMID: 37980798 DOI: 10.1016/j.compbiomed.2023.107649] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/06/2023] [Accepted: 10/31/2023] [Indexed: 11/21/2023]
Abstract
OBJECTIVE Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. APPROACH In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. MAIN RESULTS We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). SIGNIFICANCE With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.
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Affiliation(s)
- Omair Ali
- Faculty of Medicine, Department of Neurosurgery, University Hospital Knappschaftskrankenhaus Bochum GmbH, Germany; Department of Electrical Engineering and Information Technology, Ruhr-University Bochum, Germany.
| | - Muhammad Saif-Ur-Rehman
- Department of Computer Science, Ruhr-West University of Applied Science, Mülheim an der Ruhr, Germany
| | | | - Ioannis Iossifidis
- Department of Computer Science, Ruhr-West University of Applied Science, Mülheim an der Ruhr, Germany
| | - Christian Klaes
- Faculty of Medicine, Department of Neurosurgery, University Hospital Knappschaftskrankenhaus Bochum GmbH, Germany
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15
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Kancheva I, van der Salm SMA, Ramsey NF, Vansteensel MJ. Association between lesion location and sensorimotor rhythms in stroke - a systematic review with narrative synthesis. Neurol Sci 2023; 44:4263-4289. [PMID: 37606742 PMCID: PMC10641054 DOI: 10.1007/s10072-023-06982-8] [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: 11/02/2022] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Stroke causes alterations in the sensorimotor rhythms (SMRs) of the brain. However, little is known about the influence of lesion location on the SMRs. Understanding this relationship is relevant for the use of SMRs in assistive and rehabilitative therapies, such as Brain-Computer Interfaces (BCIs).. METHODS We reviewed current evidence on the association between stroke lesion location and SMRs through systematically searching PubMed and Embase and generated a narrative synthesis of findings. RESULTS We included 12 articles reporting on 161 patients. In resting-state studies, cortical and pontine damage were related to an overall decrease in alpha (∼8-12 Hz) and increase in delta (∼1-4 Hz) power. In movement paradigm studies, attenuated alpha and beta (∼15-25 Hz) event-related desynchronization (ERD) was shown in stroke patients during (attempted) paretic hand movement, compared to controls. Stronger reductions in alpha and beta ERD in the ipsilesional, compared to contralesional hemisphere, were observed for cortical lesions. Subcortical stroke was found to affect bilateral ERD and ERS, but results were highly variable. CONCLUSIONS Findings suggest a link between stroke lesion location and SMR alterations, but heterogeneity across studies and limited lesion location descriptions precluded a meta-analysis. SIGNIFICANCE Future research would benefit from more uniformly defined outcome measures, homogeneous methodologies, and improved lesion location reporting.
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Affiliation(s)
- Ivana Kancheva
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands
| | - Sandra M A van der Salm
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands
| | - Nick F Ramsey
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology & Neurosurgery, University Medical Center Utrecht, P.O. Box 85060, 3508 AB, Utrecht, The Netherlands.
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16
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Karas K, Pozzi L, Pedrocchi A, Braghin F, Roveda L. Brain-computer interface for robot control with eye artifacts for assistive applications. Sci Rep 2023; 13:17512. [PMID: 37845318 PMCID: PMC10579221 DOI: 10.1038/s41598-023-44645-y] [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: 07/07/2023] [Accepted: 10/11/2023] [Indexed: 10/18/2023] Open
Abstract
Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.
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Affiliation(s)
- Kaan Karas
- Politecnico di Milano, Department of Mechanical Engineering, via La Masa 1, 20156, Milano, Italy
| | - Luca Pozzi
- Politecnico di Milano, Department of Mechanical Engineering, via La Masa 1, 20156, Milano, Italy
| | - Alessandra Pedrocchi
- Politecnico di Milano, Department of Electronics, Information and Bioengineering, NearLab, Via Giuseppe Colombo, 40, 20133, Milan, Italy
| | - Francesco Braghin
- Politecnico di Milano, Department of Mechanical Engineering, via La Masa 1, 20156, Milano, Italy
| | - Loris Roveda
- Istituto Dalle Molle di studi sull'Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera italiana (USI), via la Santa 1, 6962, Lugano, Switzerland.
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17
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Fan S, Wu E, Cao M, Xu T, Liu T, Yang L, Su J, Liu J. Flexible In-Ga-Zn-N-O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain-computer interfaces. MATERIALS HORIZONS 2023; 10:4317-4328. [PMID: 37431592 DOI: 10.1039/d3mh00759f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain-computer interfaces (BCIs). Herein, we report the development of flexible In-Ga-Zn-N-O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.
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Affiliation(s)
- Shuangqing Fan
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Enxiu Wu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Minghui Cao
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Ting Xu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Tong Liu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Lijun Yang
- Key Laboratory of Radiopharmacokinetics for Innovative Drugs, Chinese Academy of Medical Sciences, Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, P. R. China.
| | - Jie Su
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Jing Liu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
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18
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Meng L, Jiang X, Huang J, Li W, Luo H, Wu D. User Identity Protection in EEG-Based Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3576-3586. [PMID: 37651476 DOI: 10.1109/tnsre.2023.3310883] [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: 09/02/2023]
Abstract
A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.
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19
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Eftekhar P. Clinician's Commentary on Jovanovic et al. 1. Physiother Can 2023; 75:291-292. [PMID: 37736403 PMCID: PMC10510538 DOI: 10.3138/ptc-2021-0074-cc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Affiliation(s)
- Parvin Eftekhar
- Affiliate Scientist, KITE, Toronto Rehab, University Health Network, Toronto, Ontario, Canada;
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20
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Shi X, Li B, Wang W, Qin Y, Wang H, Wang X. Classification Algorithm for Electroencephalogram-based Motor Imagery Using Hybrid Neural Network with Spatio-temporal Convolution and Multi-head Attention Mechanism. Neuroscience 2023; 527:64-73. [PMID: 37517788 DOI: 10.1016/j.neuroscience.2023.07.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/11/2023] [Accepted: 07/16/2023] [Indexed: 08/01/2023]
Abstract
Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.
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Affiliation(s)
- Xingbin Shi
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Baojiang Li
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China.
| | - Wenlong Wang
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Yuxin Qin
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Haiyan Wang
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
| | - Xichao Wang
- The School of Electrical Engineering, Shanghai DianJi University, Shanghai, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai, China
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21
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Jovanovic LI, Jervis Rademeyer H, Pakosh M, Musselman KE, Popovic MR, Marquez-Chin C. Scoping Review on Brain-Computer Interface-Controlled Electrical Stimulation Interventions for Upper Limb Rehabilitation in Adults: A Look at Participants, Interventions, and Technology. Physiother Can 2023; 75:276-290. [PMID: 37736411 PMCID: PMC10510539 DOI: 10.3138/ptc-2021-0074] [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: 07/19/2021] [Revised: 11/07/2021] [Accepted: 12/07/2021] [Indexed: 09/23/2023]
Abstract
Purpose While current rehabilitation practice for improving arm and hand function relies on physical/occupational therapy, a growing body of research evaluates the effects of technology-enhanced rehabilitation. We review interventions that combine a brain-computer interface (BCI) with electrical stimulation (ES) for upper limb movement rehabilitation to summarize the evidence on (1) populations of study participants, (2) BCI-ES interventions, and (3) the BCI-ES systems. Method After searching seven databases, two reviewers identified 23 eligible studies. We consolidated information on the study participants, interventions, and approaches used to develop integrated BCI-ES systems. The included studies investigated the use of BCI-ES interventions with stroke and spinal cord injury (SCI) populations. All studies used electroencephalography to collect brain signals for the BCI, and functional electrical stimulation was the most common type of ES. The BCI-ES interventions were typically conducted without a therapist, with sessions varying in both frequency and duration. Results Of the 23 eligible studies, only 3 studies involved the SCI population, compared to 20 involving individuals with stroke. Conclusions Future BCI-ES interventional studies could address this gap. Additionally, standardization of device and rehabilitation modalities, and study-appropriate involvement with therapists, can be considered to advance this intervention towards clinical implementation.
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Affiliation(s)
- Lazar I. Jovanovic
- From the:
Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
- The Center for Advancing Neurotechnological Innovation to Application (CRANIA), University Health Network, Toronto, Canada
| | - Hope Jervis Rademeyer
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Maureen Pakosh
- Library & Information Services, University Health Network, Toronto Rehabilitation Institute, Toronto, Canada
| | - Kristin E. Musselman
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Milos R. Popovic
- From the:
Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
- The Center for Advancing Neurotechnological Innovation to Application (CRANIA), University Health Network, Toronto, Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Cesar Marquez-Chin
- From the:
Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
- The Center for Advancing Neurotechnological Innovation to Application (CRANIA), University Health Network, Toronto, Canada
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22
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Li F, Zhang D, Chen J, Tang K, Li X, Hou Z. Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis. Front Neurosci 2023; 17:1243151. [PMID: 37732305 PMCID: PMC10507647 DOI: 10.3389/fnins.2023.1243151] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/14/2023] [Indexed: 09/22/2023] Open
Abstract
Background The incidence and mortality rates of stroke are escalating due to the growing aging population, which presents a significant hazard to human health. In the realm of stroke, brain-computer interface (BCI) technology has gained considerable attention as a means to enhance treatment efficacy and improve quality of life. Consequently, a bibliometric visualization analysis was performed to investigate the research hotspots and trends of BCI technology in stroke, with the objective of furnishing reference and guidance for future research. Methods This study utilized the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as the data source, selecting relevant literature published between 2013 and 2022 as research sample. Through the application of VOSviewer 1.6.19 and CiteSpace 6.2.R2 visualization analysis software, as well as the bibliometric online analysis platform, the scientific knowledge maps were constructed and subjected to visualization display, and statistical analysis. Results This study encompasses a total of 693 relevant literature, which were published by 2,556 scholars from 975 institutions across 53 countries/regions and have been collected by 185 journals. In the past decade, BCI technology in stroke research has exhibited an upward trend in both annual publications and citations. China and the United States are high productivity countries, while the University of Tubingen stands out as the most contributing institution. Birbaumer N and Pfurtscheller G are the authors with the highest publication and citation frequency in this field, respectively. Frontiers in Neuroscience has published the most literature, while Journal of Neural Engineering has the highest citation frequency. The research hotspots in this field cover keywords such as stroke, BCI, rehabilitation, motor imagery (MI), motor recovery, electroencephalogram (EEG), neurorehabilitation, neural plasticity, task analysis, functional electrical stimulation (FES), motor impairment, feature extraction, and induced movement therapy, which to a certain extent reflect the development trend and frontier research direction of this field. Conclusion This study comprehensively and visually presents the extensive and in-depth literature resources of BCI technology in stroke research in the form of knowledge maps, which facilitates scholars to gain a more convenient understanding of the development and prospects in this field, thereby promoting further research work.
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Affiliation(s)
- Fangcun Li
- Department of Rehabilitation Medicine, Guilin Municipal Hospital of Traditional Chinese Medicine, Guilin, China
- Graduate School, Guangxi University of Chinese Medicine, Nanning, China
| | - Ding Zhang
- Graduate School, Guangxi University of Chinese Medicine, Nanning, China
| | - Jie Chen
- Department of Pharmacy, Guilin Municipal Hospital of Traditional Chinese Medicine, Guilin, China
| | - Ke Tang
- Graduate School, Guangxi University of Chinese Medicine, Nanning, China
| | - Xiaomei Li
- Graduate School, Guangxi University of Chinese Medicine, Nanning, China
| | - Zhaomeng Hou
- Graduate School, Guangxi University of Chinese Medicine, Nanning, China
- Department of Orthopedics and Traumatology, Yancheng TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, China
- Department of Orthopedics and Traumatology, Yancheng TCM Hospital, Yancheng, China
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23
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Shao Z, Dou W, Ma D, Zhai X, Xu Q, Pan Y. A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3375-3383. [PMID: 37581962 DOI: 10.1109/tnsre.2023.3305474] [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: 08/17/2023]
Abstract
It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in [Formula: see text], revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.
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Patwardhan S, Gladhill KA, Joiner WM, Schofield JS, Lee BS, Sikdar S. Using principles of motor control to analyze performance of human machine interfaces. Sci Rep 2023; 13:13273. [PMID: 37582852 PMCID: PMC10427694 DOI: 10.1038/s41598-023-40446-5] [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: 03/31/2023] [Accepted: 08/10/2023] [Indexed: 08/17/2023] Open
Abstract
There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.
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Affiliation(s)
| | - Keri Anne Gladhill
- Department of Psychology, George Mason University, Fairfax, VA, 22030, USA
| | - Wilsaan M Joiner
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, 95616, USA
| | - Jonathon S Schofield
- Mechanical and Aerospace Engineering Department, University of California, Davis, Davis, CA, 95616, USA
| | - Ben Seiyon Lee
- Department of Statistics, George Mason University, Fairfax, VA, 22030, USA
| | - Siddhartha Sikdar
- Department of Bioengineering, George Mason University, Fairfax, VA, 22030, USA.
- Center for Adaptive Systems of Brain-Body Interactions, Fairfax, VA, 22030, USA.
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25
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Yoo KS. Motion prediction using brain waves based on artificial intelligence deep learning recurrent neural network. J Exerc Rehabil 2023; 19:219-227. [PMID: 37662525 PMCID: PMC10468292 DOI: 10.12965/jer.2346242.121] [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: 05/26/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023] Open
Abstract
Electroencephalogram (EEG) research has gained widespread use in various research domains due to its valuable insights into human body movements. In this study, we investigated the optimization of motion discrimination prediction by employing an artificial intelligence deep learning recurrent neural network (gated recurrent unit, GRU) on unique EEG data generated from specific movement types among EEG signals. The experiment involved participants categorized into five difficulty levels of postural control, targeting gymnasts in their twenties and college students majoring in physical education (n=10). Machine learning techniques were applied to extract brain-motor patterns from the collected EEG data, which consisted of 32 channels. The EEG data underwent spectrum analysis using fast Fourier transform conversion, and the GRU model network was utilized for machine learning on each EEG frequency domain, thereby improving the performance index of the learning operation process. Through the development of the GRU network algorithm, the performance index achieved up to a 15.92% improvement compared to the accuracy of existing models, resulting in motion recognition accuracy ranging from a minimum of 94.67% to a maximum of 99.15% between actual and predicted values. These optimization outcomes are attributed to the enhanced accuracy and cost function of the GRU network algorithm's hidden layers. By implementing motion identification optimization based on artificial intelligence machine learning results from EEG signals, this study contributes to the emerging field of exercise rehabilitation, presenting an innovative paradigm that reveals the interconnectedness between the brain and the science of exercise.
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Affiliation(s)
- Kyoung-Seok Yoo
- Department of Sport Sciences, Hannam University, Daejeon,
Korea
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26
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Xie X, Xu Z, Yu X, Jiang H, Li H, Feng W. Liquid-in-liquid printing of 3D and mechanically tunable conductive hydrogels. Nat Commun 2023; 14:4289. [PMID: 37463898 DOI: 10.1038/s41467-023-40004-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 07/06/2023] [Indexed: 07/20/2023] Open
Abstract
Conductive hydrogels require tunable mechanical properties, high conductivity and complicated 3D structures for advanced functionality in (bio)applications. Here, we report a straightforward strategy to construct 3D conductive hydrogels by programable printing of aqueous inks rich in poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) inside of oil. In this liquid-in-liquid printing method, assemblies of PEDOT:PSS colloidal particles originating from the aqueous phase and polydimethylsiloxane surfactants from the other form an elastic film at the liquid-liquid interface, allowing trapping of the hydrogel precursor inks in the designed 3D nonequilibrium shapes for subsequent gelation and/or chemical cross-linking. Conductivities up to 301 S m-1 are achieved for a low PEDOT:PSS content of 9 mg mL-1 in two interpenetrating hydrogel networks. The effortless printability enables us to tune the hydrogels' components and mechanical properties, thus facilitating the use of these conductive hydrogels as electromicrofluidic devices and to customize near-field communication (NFC) implantable biochips in the future.
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Affiliation(s)
- Xinjian Xie
- College of Polymer Science and Engineering, Sichuan University, 610065, Chengdu, China
| | - Zhonggang Xu
- College of Polymer Science and Engineering, Sichuan University, 610065, Chengdu, China
| | - Xin Yu
- Department of Pancreatic Surgery, Department of Biotherapy, West China Hospital, Sichuan University, 610065, Chengdu, China
| | - Hong Jiang
- Department of Pancreatic Surgery, Department of Biotherapy, West China Hospital, Sichuan University, 610065, Chengdu, China
| | - Hongjiao Li
- College of Chemical Engineering, Sichuan University, 610065, Chengdu, China.
| | - Wenqian Feng
- College of Polymer Science and Engineering, Sichuan University, 610065, Chengdu, China.
- State Key Laboratory of Polymer Materials Engineering, Sichuan University, 610065, Chengdu, China.
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27
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Seifpour S, Šatka A. Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report. Brain Sci 2023; 13:1013. [PMID: 37508946 PMCID: PMC10377314 DOI: 10.3390/brainsci13071013] [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: 05/10/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process.
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Affiliation(s)
- Saman Seifpour
- RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
- Institute of Measurement Science, Slovak Academy of Sciences, Dubravska cesta 9, 84104 Bratislava, Slovakia
| | - Alexander Šatka
- Institute of Measurement Science, Slovak Academy of Sciences, Dubravska cesta 9, 84104 Bratislava, Slovakia
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28
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Wang W, Shi B, Wang D, Wang J, Liu G. Enhanced lower-limb motor imagery by kinesthetic illusion. Front Neurosci 2023; 17:1077479. [PMID: 37409102 PMCID: PMC10319417 DOI: 10.3389/fnins.2023.1077479] [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: 10/23/2022] [Accepted: 05/30/2023] [Indexed: 07/07/2023] Open
Abstract
Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.
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Affiliation(s)
- Weizhen Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Bin Shi
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Dong Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Gang Liu
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
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29
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Nam H, Kim JM, Choi W, Bak S, Kam TE. The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets. Front Hum Neurosci 2023; 17:1205881. [PMID: 37342822 PMCID: PMC10277566 DOI: 10.3389/fnhum.2023.1205881] [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: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Introduction The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. Methods In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. Results and discussion The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.
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Affiliation(s)
| | | | | | | | - Tae-Eui Kam
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
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30
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Kurkin S, Gordleeva S, Savosenkov A, Grigorev N, Smirnov N, Grubov VV, Udoratina A, Maksimenko V, Kazantsev V, Hramov AE. Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery. SENSORS (BASEL, SWITZERLAND) 2023; 23:4661. [PMID: 37430576 DOI: 10.3390/s23104661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 07/12/2023]
Abstract
Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study. Participants were randomly assigned to receive sham (15 subjects) or real high-frequency rTMS (15 subjects). We performed EEG sensor-level, source-level, and connectivity analyses to evaluate the rTMS effects. We revealed that excitatory stimulation of the left DLPFC increases theta-band power in the right precuneus (PrecuneusR) via the functional connectivity between them. The precuneus theta-band power negatively correlates with the latency of the MI response, so the rTMS speeds up the responses in 50% of participants. We suppose that posterior theta-band power reflects attention modulation of sensory processing; therefore, high power may indicate attentive processing and cause faster responses.
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Affiliation(s)
- Semen Kurkin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Susanna Gordleeva
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Andrey Savosenkov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Nikita Grigorev
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Nikita Smirnov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Vadim V Grubov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Anna Udoratina
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Vladimir Maksimenko
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Victor Kazantsev
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Alexander E Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
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31
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Chen J, Zhang Y, Pan Y, Xu P, Guan C. A transformer-based deep neural network model for SSVEP classification. Neural Netw 2023; 164:521-534. [PMID: 37209444 DOI: 10.1016/j.neunet.2023.04.045] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/24/2023] [Accepted: 04/26/2023] [Indexed: 05/22/2023]
Abstract
Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data becomes urgent. In recent years, developing the methods that can work in inter-subject scenario has become a promising new direction. As a popular deep learning model nowadays, Transformer has been used in EEG signal classification tasks owing to its excellent performance. Therefore, in this study, we proposed a deep learning model for SSVEP classification based on Transformer architecture in inter-subject scenario, termed as SSVEPformer, which was the first application of Transformer on the SSVEP classification. Inspired by previous studies, we adopted the complex spectrum features of SSVEP data as the model input, which could enable the model to simultaneously explore the spectral and spatial information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) was proposed to improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12 targets; Dataset 2: 35 subjects, 40 targets). The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate than other baseline methods. The proposed models validate the feasibility of deep learning models based on Transformer architecture for SSVEP data classification, and could serve as potential models to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
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Affiliation(s)
- Jianbo Chen
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, China
| | - Yangsong Zhang
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, China; MOE Key Laboratory for NeuroInformation, Clinical Hospital of Chengdu Brain Science Institute, and Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yudong Pan
- School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang, China
| | - Peng Xu
- MOE Key Laboratory for NeuroInformation, Clinical Hospital of Chengdu Brain Science Institute, and Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore
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32
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Asirvatham T. Comment on Lee et al. (2021): Effects of Robot-Assisted Rehabilitation on Hand Function of People With Stroke. Am J Occup Ther 2023; 77:24112. [PMID: 37224523 DOI: 10.5014/ajot.2023.50302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023] Open
Affiliation(s)
- Thajus Asirvatham
- Thajus Asirvatham, OTR, is Occupational Therapy Specialist, Department of Occupational Therapy, Qatar Rehabilitation Institute, Hamad Medical Corporation, Doha, Qatar;
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Kim E, Lee WH, Seo HG, Nam HS, Kim YJ, Kang MG, Bang MS, Kim S, Oh BM. Deciphering Functional Connectivity Differences Between Motor Imagery and Execution of Target-Oriented Grasping. Brain Topogr 2023; 36:433-446. [PMID: 37060497 DOI: 10.1007/s10548-023-00956-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 03/20/2023] [Indexed: 04/16/2023]
Abstract
This study aimed to delineate overlapping and distinctive functional connectivity in visual motor imagery, kinesthetic motor imagery, and motor execution of target-oriented grasping action of the right hand. Functional magnetic resonance imaging data were obtained from 18 right-handed healthy individuals during each condition. Seed-based connectivity and multi-voxel pattern analyses were employed after selecting seed regions with the left primary motor cortex and supplementary motor area. There was equivalent seed-based connectivity during the three conditions in the bilateral frontoparietal and temporal areas. When the seed region was the left primary motor cortex, increased connectivity was observed in the left cuneus and superior frontal area during visual and kinesthetic motor imageries, respectively, compared with that during motor execution. Multi-voxel pattern analyses revealed that each condition was differentiated by spatially distributed connectivity patterns of the left primary motor cortex within the right cerebellum VI, cerebellum crus II, and left lingual area. When the seed region was the left supplementary motor area, the connectivity patterns within the right putamen, thalamus, cerebellar areas IV-V, and left superior parietal lobule were significantly classified above chance level across the three conditions. The present findings improve our understanding of the spatial representation of functional connectivity and its specific patterns among motor imagery and motor execution. The strength and fine-grained connectivity patterns of the brain areas can discriminate between motor imagery and motor execution.
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Affiliation(s)
- Eunkyung Kim
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Woo Hyung Lee
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Han Gil Seo
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyung Seok Nam
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yoon Jae Kim
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Min-Gu Kang
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Moon Suk Bang
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- National Traffic Injury Rehabilitation Hospital, Yangpyeong, Republic of Korea
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Institute of Bioengineering, Seoul National University, Seoul, Republic of Korea.
| | - Byung-Mo Oh
- Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- National Traffic Injury Rehabilitation Hospital, Yangpyeong, Republic of Korea.
- Institute on aging, Seoul National University, Seoul, Republic of Korea.
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Zafar A, Hussain SJ, Ali MU, Lee SW. Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073714. [PMID: 37050774 PMCID: PMC10098559 DOI: 10.3390/s23073714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 06/01/2023]
Abstract
In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Shaik Javeed Hussain
- Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Zhu J, Zhu L, Ding W, Ying N, Xu P, Zhang J. An improved feature extraction method using low-rank representation for motor imagery classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Si X, Zhou Y, Li S, Zhang X, Han S, Xiang S, Ming D. Brain-Computer Interfaces in Visualized Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1199:127-153. [PMID: 37460730 DOI: 10.1007/978-981-32-9902-3_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
The brain-computer interface (BCI), also known as a brain-machine interface (BMI), has attracted extensive attention in biomedical applications. More importantly, BCI technologies have substantially revolutionized early predictions, diagnostic techniques, and rehabilitation strategies addressing acute diseases because of BCI's innovations and clinical translations. Therefore, in this chapter, a comprehensive description of the basic concepts of BCI will be exhibited, and various visualization techniques employed in BCI's medical applications will be discussed.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.
| | - Yu Zhou
- College of Medical Technology and Engineering, Henan University of Science and Technology, Henan, China
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Shunli Han
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Shaoxin Xiang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
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Brain-machine Interface (BMI)-based Neurorehabilitation for Post-stroke Upper Limb Paralysis. Keio J Med 2022; 71:82-92. [PMID: 35718470 DOI: 10.2302/kjm.2022-0002-oa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Because recovery from upper limb paralysis after stroke is challenging, compensatory approaches have been the main focus of upper limb rehabilitation. However, based on fundamental and clinical research indicating that the brain has a far greater potential for plastic change than previously thought, functional restorative approaches have become increasingly common. Among such interventions, constraint-induced movement therapy, task-specific training, robotic therapy, neuromuscular electrical stimulation (NMES), mental practice, mirror therapy, and bilateral arm training are recommended in recently published stroke guidelines. For severe upper limb paralysis, however, no effective therapy has yet been established. Against this background, there is growing interest in applying brain-machine interface (BMI) technologies to upper limb rehabilitation. Increasing numbers of randomized controlled trials have demonstrated the effectiveness of BMI neurorehabilitation, and several meta-analyses have shown medium to large effect sizes with BMI therapy. Subgroup analyses indicate higher intervention effects in the subacute group than the chronic group, when using movement attempts as the BMI-training trigger task rather than using motor imagery, and using NMES as the external device compared with using other devices. The Keio BMI team has developed an electroencephalography-based neurorehabilitation system and has published clinical and basic studies demonstrating its effectiveness and neurophysiological mechanisms. For its wider clinical application, the positioning of BMI therapy in upper limb rehabilitation needs to be clarified, BMI needs to be commercialized as an easy-to-use and cost-effective medical device, and training systems for rehabilitation professionals need to be developed. A technological breakthrough enabling selective modulation of neural circuits is also needed.
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EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review. Neurol Int 2022; 14:1046-1061. [PMID: 36548189 PMCID: PMC9782188 DOI: 10.3390/neurolint14040084] [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: 11/16/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND There is increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community. METHODS We performed an electronic search in Scopus, looking for studies reporting on rehabilitation in patients with neurological disabilities. We used the most influential papers to outline the knowledge base and carried out a word co-occurrence analysis to identify the research hotspots. We also used depicted collaboration networks between universities, authors, and countries after analyzing the cocitations. The results were presented in summary tables, plots, and maps. Finally, a content review based on the top-20 most cited articles completed our study. RESULTS Our current bibliometric study was based on 874 records from 420 sources. There was vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by J. Daly and J.R. Wolpaw (708 citations). The US, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of functional magnetic imaging to EEG-based brain-machine interface, motor imagery, and deep learning. CONCLUSIONS EEG constitutes the most significant input in brain-computer interfaces (BCIs) and can be successfully used in the neurorehabilitation of patients with stroke symptoms, amyotrophic lateral sclerosis, and traumatic brain and spinal injuries. EEG-based BCI facilitates the training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG-filtering algorithms.
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Bibliometric analysis on Brain-computer interfaces in a 30-year period. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04226-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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de Seta V, Toppi J, Colamarino E, Molle R, Castellani F, Cincotti F, Mattia D, Pichiorri F. Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients. Front Hum Neurosci 2022; 16:1016862. [PMID: 36483633 PMCID: PMC9722732 DOI: 10.3389/fnhum.2022.1016862] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/26/2022] [Indexed: 10/05/2023] Open
Abstract
Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.
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Affiliation(s)
- Valeria de Seta
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Jlenia Toppi
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Emma Colamarino
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Rita Molle
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Filippo Castellani
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Febo Cincotti
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Floriana Pichiorri
- Neuroelectric Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
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Syrov N, Yakovlev L, Nikolaeva V, Kaplan A, Lebedev M. Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option. Diagnostics (Basel) 2022; 12:2607. [PMID: 36359454 PMCID: PMC9689852 DOI: 10.3390/diagnostics12112607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/23/2023] Open
Abstract
Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation.
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Affiliation(s)
- Nikolay Syrov
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Lev Yakovlev
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Varvara Nikolaeva
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
| | - Alexander Kaplan
- Baltic Center for Neurotechnology and Artificial Intellect, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Laboratory for Neurophysiology and Neuro-Computer Interfaces, Human and Animal Physiology Department, School of Biology, M.V. Lomonosov Moscow State University, 119234 Moscow, Russia
| | - Mikhail Lebedev
- V. Zelman Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
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Huang Y, Gong Y, Liu Y, Lu J. Global trends and hot topics in electrical stimulation of skeletal muscle research over the past decade: A bibliometric analysis. Front Neurol 2022; 13:991099. [PMID: 36277916 PMCID: PMC9581161 DOI: 10.3389/fneur.2022.991099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/13/2022] [Indexed: 11/25/2022] Open
Abstract
Background Over the past decade, numerous advances have been made in the research on electrical stimulation of skeletal muscle. However, the developing status and future direction of this field remain unclear. This study aims to visualize the evolution and summarize global research hot topics and trends based on quantitative and qualitative evidence from bibliometrics. Methods Literature search was based on the Web of Science Core Collection (WoSCC) database from 2011 to 2021. CiteSpace and VOSviewer, typical bibliometric tools, were used to perform analysis and visualization. Results A total of 3,059 documents were identified. The number of literature is on the rise in general. Worldwide, researchers come primarily from North America and Europe, represented by the USA, France, Switzerland, and Canada. The Udice French Research Universities is the most published affiliation. Millet GY and Maffiuletti NA are the most prolific and the most co-cited authors, respectively. Plos One is the most popular journal, and the Journal of Applied Physiology is the top co-cited journal. The main keywords are muscle fatigue, neuromuscular electrical stimulation, spinal cord injury, tissue engineering, and atrophy. Moreover, this study systematically described the hotspots in this field. Conclusion As the first bibliometric analysis of electrical stimulation of skeletal muscle research over the past decade, this study can help scholars recognize hot topics and trends and provide a reference for further exploration in this field.
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Kwon J, Hwang J, Nam H, Im CH. Novel hybrid visual stimuli incorporating periodic motions into conventional flickering or pattern-reversal visual stimuli for steady-state visual evoked potential-based brain-computer interfaces. Front Neuroinform 2022; 16:997068. [PMID: 36213545 PMCID: PMC9534124 DOI: 10.3389/fninf.2022.997068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.
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Affiliation(s)
- Jinuk Kwon
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Jihun Hwang
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Hyerin Nam
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea
- Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea
- *Correspondence: Chang-Hwan Im
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Song M, Jeong H, Kim J, Jang SH, Kim J. An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study. Front Neurorobot 2022; 16:971547. [PMID: 36172602 PMCID: PMC9510756 DOI: 10.3389/fnbot.2022.971547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/08/2022] [Indexed: 11/22/2022] Open
Abstract
Many studies have used motor imagery-based brain–computer interface (MI-BCI) systems for stroke rehabilitation to induce brain plasticity. However, they mainly focused on detecting motor imagery but did not consider the effect of false positive (FP) detection. The FP could be a threat to patients with stroke as it can induce wrong-directed brain plasticity that would result in adverse effects. In this study, we proposed a rehabilitative MI-BCI system that focuses on rejecting the FP. To this end, we first identified numerous electroencephalogram (EEG) signals as the causes of the FP, and based on the characteristics of the signals, we designed a novel two-phase classifier using a small number of EEG channels, including the source of the FP. Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71.76% selectivity and 13.70% FP rate by using only four EEG channels in the patient group with stroke. Moreover, our system can compensate for day-to-day variations for prolonged session intervals by recalibration. The results suggest that our proposed system, a practical approach for the clinical setting, could improve the therapeutic effect of MI-BCI by reducing the adverse effect of the FP.
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Affiliation(s)
- Minsu Song
- Department of Medical Device, Korea Institute of Machinery and Materials, Daegu, South Korea
| | - Hojun Jeong
- School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi-do, South Korea
| | - Jongbum Kim
- Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Sung-Ho Jang
- Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Jonghyun Kim
- School of Mechanical Engineering, Sungkyunkwan University, Gyeonggi-do, South Korea
- *Correspondence: Jonghyun Kim
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Jia J. Exploration on neurobiological mechanisms of the central–peripheral–central closed-loop rehabilitation. Front Cell Neurosci 2022; 16:982881. [PMID: 36119128 PMCID: PMC9479450 DOI: 10.3389/fncel.2022.982881] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/12/2022] [Indexed: 11/13/2022] Open
Abstract
Central and peripheral interventions for brain injury rehabilitation have been widely employed. However, as patients’ requirements and expectations for stroke rehabilitation have gradually increased, the limitations of simple central intervention or peripheral intervention in the rehabilitation application of stroke patients’ function have gradually emerged. Studies have suggested that central intervention promotes the activation of functional brain regions and improves neural plasticity, whereas peripheral intervention enhances the positive feedback and input of sensory and motor control modes to the central nervous system, thereby promoting the remodeling of brain function. Based on the model of a central–peripheral–central (CPC) closed loop, the integration of center and peripheral interventions was effectively completed to form “closed-loop” information feedback, which could be applied to specific brain areas or function-related brain regions of patients. Notably, the closed loop can also be extended to central and peripheral immune systems as well as central and peripheral organs such as the brain–gut axis and lung–brain axis. In this review article, the model of CPC closed-loop rehabilitation and the potential neuroimmunological mechanisms of a closed-loop approach will be discussed. Further, we highlight critical questions about the neuroimmunological aspects of the closed-loop technique that merit future research attention.
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Affiliation(s)
- Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Regional Medical Center, Fujian, China
- The First Affiliated Hospital of Fujian Medical University, Fujian, China
- *Correspondence: Jie Jia,
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Fabietti M, Mahmud M, Lotfi A, Kaiser MS. ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals. Brain Inform 2022; 9:19. [PMID: 36048345 PMCID: PMC9437165 DOI: 10.1186/s40708-022-00167-3] [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: 04/25/2022] [Accepted: 07/22/2022] [Indexed: 11/10/2022] Open
Abstract
Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.
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Affiliation(s)
- Marcos Fabietti
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK. .,Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK. .,Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
| | - Ahmad Lotfi
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - M Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Dhaka, 1342, Savar, Bangladesh
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Pereira JA, Ray A, Rana M, Silva C, Salinas C, Zamorano F, Irani M, Opazo P, Sitaram R, Ruiz S. A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study. Front Hum Neurosci 2022; 16:933559. [PMID: 36092645 PMCID: PMC9452730 DOI: 10.3389/fnhum.2022.933559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.
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Affiliation(s)
- Jaime A. Pereira
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Andreas Ray
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Mohit Rana
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Claudio Silva
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Cesar Salinas
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
| | - Francisco Zamorano
- Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Facultad de Medicina, Clínica Alemana- Universidad del Desarrollo, Santiago, Chile
- Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Martin Irani
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Patricia Opazo
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
- *Correspondence: Ranganatha Sitaram
| | - Sergio Ruiz
- Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile
- Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
- Sergio Ruiz
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Cao L, Wang W, Huang C, Xu Z, Wang H, Jia J, Chen S, Dong Y, Fan C, de Albuquerque VHC. An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2264-2274. [PMID: 35969547 DOI: 10.1109/tnsre.2022.3198434] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.
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Yao L, Jiang N, Mrachacz-Kersting N, Zhu X, Farina D, Wang Y. Reducing the Calibration Time in Somatosensory BCI by Using Tactile ERD. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1870-1876. [PMID: 35767500 DOI: 10.1109/tnsre.2022.3184402] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE We propose a tactile-induced-oscillation approach to reduce the calibration time in somatosensory brain-computer interfaces (BCI). METHODS Based on the similarity between tactile induced event-related desynchronization (ERD) and imagined sensation induced ERD activation, we extensively evaluated BCI performance when using a conventional and a novel calibration strategy. In the conventional calibration, the tactile imagined data was used, while in the sensory calibration model sensory stimulation data was used. Subjects were required to sense the tactile stimulus when real tactile was applied to the left or right wrist and were required to perform imagined sensation tasks in the somatosensory BCI paradigm. RESULTS The sensory calibration led to a significantly better performance than the conventional calibration when tested on the same imagined sensation dataset ( [Formula: see text]=10.89, P=0.0038), with an average 5.1% improvement in accuracy. Moreover, the sensory calibration was 39.3% faster in reaching a performance level of above 70% accuracy. CONCLUSION The proposed approach of using tactile ERD from the sensory cortex provides an effective way of reducing the calibration time in a somatosensory BCI system. SIGNIFICANCE The tactile stimulation would be specifically useful before BCI usage, avoiding excessive fatigue when the mental task is difficult to perform. The tactile ERD approach may find BCI applications for patients or users with preserved afferent pathways.
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Remsik AB, van Kan PLE, Gloe S, Gjini K, Williams L, Nair V, Caldera K, Williams JC, Prabhakaran V. BCI-FES With Multimodal Feedback for Motor Recovery Poststroke. Front Hum Neurosci 2022; 16:725715. [PMID: 35874158 PMCID: PMC9296822 DOI: 10.3389/fnhum.2022.725715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 05/26/2022] [Indexed: 01/31/2023] Open
Abstract
An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals-user-generated intent-to-move neural activity recorded from cerebral cortical motor areas-to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity.
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Affiliation(s)
- Alexander B. Remsik
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- School of Medicine and Public Health, Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Peter L. E. van Kan
- Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
| | - Shawna Gloe
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Klevest Gjini
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
| | - Leroy Williams
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Educational Psychology, University of Wisconsin–Madison, Madison, WI, United States
| | - Veena Nair
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
| | - Kristin Caldera
- Department of Orthopedics and Rehabilitation, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
| | - Justin C. Williams
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurological Surgery, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
- Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
- Medical Scientist Training Program, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
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