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Liu L, Li J, Ouyang R, Zhou D, Fan C, Liang W, Li F, Lv Z, Wu X. Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton. J Neurosci Methods 2024; 406:110132. [PMID: 38604523 DOI: 10.1016/j.jneumeth.2024.110132] [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: 11/01/2023] [Revised: 03/11/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling. NEW METHOD Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients. COMPARISON WITH EXISTING METHODS In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system. RESULTS In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively. CONCLUSION Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.
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Affiliation(s)
- Lei Liu
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China
| | - Jian Li
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China
| | - Rui Ouyang
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China
| | - Danya Zhou
- National Centre for International Research in Cell and Gene Therapy, School of Basic Medical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Cunhang Fan
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China.
| | - Wen Liang
- Google Inc, United States of America
| | - Fan Li
- Civil Aviation Flight University of China, China
| | - Zhao Lv
- Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China; Civil Aviation Flight University of China, China
| | - Xiaopei Wu
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China.
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Awuah WA, Ahluwalia A, Darko K, Sanker V, Tan JK, Tenkorang PO, Ben-Jaafar A, Ranganathan S, Aderinto N, Mehta A, Shah MH, Lee Boon Chun K, Abdul-Rahman T, Atallah O. Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications. World Neurosurg 2024; 189:138-153. [PMID: 38789029 DOI: 10.1016/j.wneu.2024.05.104] [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: 01/22/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024]
Abstract
Brain-computer interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography in 1924. These interfaces effectively convert central nervous system signals into commands for external devices, offering revolutionary benefits to patients with severe communication and motor impairments due to a myriad of neurological conditions like stroke, spinal cord injuries, and neurodegenerative disorders. BCIs enable these individuals to communicate and interact with their environment, using their brain signals to operate interfaces for communication and environmental control. This technology is especially crucial for those completely locked in, providing a communication lifeline where other methods fall short. The advantages of BCIs are profound, offering autonomy and an improved quality of life for patients with severe disabilities. They allow for direct interaction with various devices and prostheses, bypassing damaged or nonfunctional neural pathways. However, challenges persist, including the complexity of accurately interpreting brain signals, the need for individual calibration, and ensuring reliable, long-term use. Additionally, ethical considerations arise regarding autonomy, consent, and the potential for dependence on technology. Despite these challenges, BCIs represent a transformative development in neurotechnology, promising enhanced patient outcomes and a deeper understanding of brain-machine interfaces.
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Affiliation(s)
| | - Arjun Ahluwalia
- School of Medicine, Queen's University Belfast, Belfast, United Kingdom
| | - Kwadwo Darko
- Department of Neurosurgery, Korle Bu Teaching Hospital, Accra, Ghana
| | - Vivek Sanker
- Department of Neurosurgery, Trivandrum Medical College, India
| | - Joecelyn Kirani Tan
- Faculty of Medicine, University of St Andrews, St. Andrews, Scotland, United Kingdom.
| | | | - Adam Ben-Jaafar
- University College Dublin, School of Medicine, Belfield, Dublin, Ireland
| | - Sruthi Ranganathan
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas Aderinto
- Internal Medicine Department, LAUTECH Teaching Hospital, Ogbomoso, Nigeria
| | - Aashna Mehta
- University of Debrecen-Faculty of Medicine, Debrecen, Hungary
| | | | | | | | - Oday Atallah
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
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Xie X, Shi R, Yu H, Wan X, Liu T, Duan D, Li D, Wen D. Executive function rehabilitation and evaluation based on brain-computer interface and virtual reality: our opinion. Front Neurosci 2024; 18:1377097. [PMID: 38808030 PMCID: PMC11130371 DOI: 10.3389/fnins.2024.1377097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/29/2024] [Indexed: 05/30/2024] Open
Affiliation(s)
- Xueguang Xie
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Ruihang Shi
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, China
| | - Hao Yu
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Xianglong Wan
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Tiange Liu
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Dingna Duan
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Danyang Li
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Dong Wen
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
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Deng H, Li M, Li J, Guo M, Xu G. A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding. J Neurosci Methods 2024; 405:110108. [PMID: 38458260 DOI: 10.1016/j.jneumeth.2024.110108] [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: 11/05/2023] [Revised: 02/28/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024]
Abstract
BACKGROUND Motor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects' optimal hyperparameters. NEW METHODS This study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects. RESULTS The MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust. CONCLUSIONS The combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI's utility and robustness.
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Affiliation(s)
- Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China.
| | - Jundi Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Miaomiao Guo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China; Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin 300132, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300132, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China
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Lo YT, Lim MJR, Kok CY, Wang S, Blok SZ, Ang TY, Ng VYP, Rao JP, Chua KSG. Neural Interface-Based Motor Neuroprosthesis in Poststroke Upper Limb Neurorehabilitation: An Individual Patient Data Meta-analysis. Arch Phys Med Rehabil 2024:S0003-9993(24)00910-9. [PMID: 38579958 DOI: 10.1016/j.apmr.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/07/2024]
Abstract
OBJECTIVE To determine the efficacy of neural interface-based neurorehabilitation, including brain-computer interface, through conventional and individual patient data (IPD) meta-analysis and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation. DATA SOURCES PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed. STUDY SELECTION Studies using neural interface-controlled physical effectors (functional electrical stimulation and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper-extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed. DATA EXTRACTION Changes in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD. DATA SYNTHESIS Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE scores increased by a mean of 5.23 (95% confidence interval [CI]: 3.85-6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 vs ≤4 weeks. On IPD analysis, the mean time-to-improvement above minimal clinically important difference (MCID) was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41%-70%). Patients with severe impairment (P=.042) and age >50 years (P=.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (hazard ratio [HR] 0.15, P=.08 and HR 0.47, P=.06, respectively). CONCLUSION Neural interface-based motor rehabilitation resulted in significant, although modest, reductions in poststroke impairment and should be considered for wider applications in stroke neurorehabilitation.
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Affiliation(s)
- Yu Tung Lo
- Department of Neurosurgery, National Neuroscience Institute; Duke-NUS Medical School.
| | - Mervyn Jun Rui Lim
- Department of Neurosurgery, National University Hospital; National University of Singapore, Yong Loo Lin School of Medicine
| | - Chun Yen Kok
- Department of Neurosurgery, National Neuroscience Institute
| | - Shilin Wang
- Department of Neurosurgery, National Neuroscience Institute
| | | | - Ting Yao Ang
- Department of Neurosurgery, National Neuroscience Institute
| | | | - Jai Prashanth Rao
- Department of Neurosurgery, National Neuroscience Institute; Duke-NUS Medical School
| | - Karen Sui Geok Chua
- National University of Singapore, Yong Loo Lin School of Medicine; Institute of Rehabilitation Excellence, Tan Tock Seng Hospital Rehabilitation Centre; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Deng H, Li M, Zuo H, Zhou H, Qi E, Wu X, Xu G. Personalized motor imagery prediction model based on individual difference of ERP. J Neural Eng 2024; 21:016027. [PMID: 38359457 DOI: 10.1088/1741-2552/ad29d6] [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: 07/28/2023] [Accepted: 02/15/2024] [Indexed: 02/17/2024]
Abstract
Objective. Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference.Approach.A novel paradigm named action observation-based multi-delayed matching posture task evokes ERP during a delayed matching posture task phase by retrieving picture stimuli and videos, and generates MI electroencephalogram through action observation and autonomous imagery in an action observation-based motor imagery phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual's suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods.Main results.The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual's ERP amplitude and event-related desynchronization (ERD) intensity are the largest, which helps to improve the accuracy by 14.25%.Significance.The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual's MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.
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Affiliation(s)
- Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Haoxin Zuo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Huihui Zhou
- Peng Cheng Laboratory, 518000 Shenzhen, People's Republic of China
| | - Enming Qi
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Xue Wu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, 300132 Tianjin, People's Republic of China
- School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, 300132 Tianjin, People's Republic of China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, 300132 Tianjin, People's Republic of China
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Duan D, Wu Z, Zhou Y, Wan X, Wen D. Working memory training and evaluation based on brain-computer interface and virtual reality: our opinion. Front Hum Neurosci 2023; 17:1291983. [PMID: 37941569 PMCID: PMC10627994 DOI: 10.3389/fnhum.2023.1291983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Affiliation(s)
- Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhonglin Wu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
| | - Xianglong Wan
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
| | - Dong Wen
- School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China
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Zhi H, Yu Z, Yu T, Gu Z, Yang J. A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3988-3998. [PMID: 37815970 DOI: 10.1109/tnsre.2023.3323325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and lead to inaccurate decoding outcomes due to the redundant information. To address this limitation and make full use of the multi-domain EEG features, a multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) is proposed for MI decoding. The proposed network provides a novel mechanism that utilize the spatial and temporal EEG features combined with frequency and time-frequency characteristics. This network enables powerful feature extraction without complicated network structure. Specifically, the TSFCNet first employs the MixConv-Residual block to extract multiscale temporal features from multi-band filtered EEG data. Next, the temporal-spatial-frequency convolution block implements three shallow, parallel and independent convolutional operations in spatial, frequency and time-frequency domain, and captures high discriminative representations from these domains respectively. Finally, these features are effectively aggregated by average pooling layers and variance layers, and the network is trained with the joint supervision of the cross-entropy and the center loss. Our experimental results show that the TSFCNet outperforms the state-of-the-art models with superior classification accuracy and kappa values (82.72% and 0.7695 for dataset BCI competition IV 2a, 86.39% and 0.7324 for dataset BCI competition IV 2b). These competitive results demonstrate that the proposed network is promising for enhancing the decoding performance of MI BCIs.
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Swami P, Gramann K, Vonstad EK, Vereijken B, Holt A, Holt T, Sandstrak G, Nilsen JH, Su X. CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses. Front Hum Neurosci 2023; 17:1223774. [PMID: 37795210 PMCID: PMC10546026 DOI: 10.3389/fnhum.2023.1223774] [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: 05/16/2023] [Accepted: 08/31/2023] [Indexed: 10/06/2023] Open
Abstract
To investigate event-related activity in human brain dynamics as measured with EEG, triggers must be incorporated to indicate the onset of events in the experimental protocol. Such triggers allow for the extraction of ERP, i.e., systematic electrophysiological responses to internal or external stimuli that must be extracted from the ongoing oscillatory activity by averaging several trials containing similar events. Due to the technical setup with separate hardware sending and recording triggers, the recorded data commonly involves latency differences between the transmitted and received triggers. The computation of these latencies is critical for shifting the epochs with respect to the triggers sent. Otherwise, timing differences can lead to a misinterpretation of the resulting ERPs. This study presents a methodical approach for the CLET using a photodiode on a non-immersive VR (i.e., LED screen) and an immersive VR (i.e., HMD). Two sets of algorithms are proposed to analyze the photodiode data. The experiment designed for this study involved the synchronization of EEG, EMG, PPG, photodiode sensors, and ten 3D MoCap cameras with a VR presentation platform (Unity). The average latency computed for LED screen data for a set of white and black stimuli was 121.98 ± 8.71 ms and 121.66 ± 8.80 ms, respectively. In contrast, the average latency computed for HMD data for the white and black stimuli sets was 82.80 ± 7.63 ms and 69.82 ± 5.52 ms. The codes for CLET and analysis, along with datasets, tables, and a tutorial video for using the codes, have been made publicly available.
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Affiliation(s)
- Piyush Swami
- Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
- Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- Biomedical Engineering Techies, Broendby, Denmark
| | - Klaus Gramann
- Biological Psychology and Neuroergonomics, Technical University of Berlin, Berlin, Germany
| | - Elise Klæbo Vonstad
- Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Alexander Holt
- Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tomas Holt
- Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Grethe Sandstrak
- Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jan Harald Nilsen
- Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Xiaomeng Su
- Motion Capture and Visualization Laboratory, Applied Information Technology Group, Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
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Shou YZ, Wang XH, Yang GF. Verum versus Sham brain-computer interface on upper limb function recovery after stroke: A systematic review and meta-analysis of randomized controlled trials. Medicine (Baltimore) 2023; 102:e34148. [PMID: 37390271 PMCID: PMC10313240 DOI: 10.1097/md.0000000000034148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 06/08/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND Previous clinical trials have reported that the brain-computer interface (BCI) is a useful management tool for upper limb function recovery (ULFR) in stroke. However, there is insufficient evidence regarding this topic. Thus, this study aimed to investigate the effectiveness of verum versus sham BCI on the ULFR in stroke patients. METHODS We comprehensively searched the Cochrane Library, PUBMED, EMBASE, Web of Science, and China National Knowledge Infrastructure databases from their inception to January 1, 2023. Randomized clinical trials (RCTs) assessing the effectiveness and safety of BCI for ULFR after stroke were included. The outcomes were the Fugl-Meyer Assessment for Upper Extremity, Wolf Motor Function Test, Modified Barthel Index, motor activity log, and Action Research Arm Test. The methodological quality of all the included randomized controlled trials was evaluated using the Cochrane risk-of-bias tool. Statistical analysis was performed using RevMan 5.4 software. RESULTS Eleven eligible studies involving 334 patients were included. The results of the meta-analysis showed significant differences in the Fugl-Meyer Assessment for Upper Extremity (mean difference [MD] = 4.78, 95% confidence interval [CI] [1.90, 7.65], I2 = 0%, P = .001) and Modified Barthel Index (MD = 7.37, 95% CI [1.89, 12.84], I2 = 19%, P = .008). However, no significant differences were found on motor activity log (MD = -0.70, 95% CI [-3.17, 1.77]), Action Research Arm Test (MD = 3.05, 95% CI [-8.33, 14.44], I2 = 0%, P = .60), and Wolf Motor Function Test (MD = 4.23, 95% CI [-0.55, 9.01], P = .08). CONCLUSION BCI may be an effective management strategy for ULFR in stroke patients. Future studies with larger sample size and strict design are still needed to warrant the current findings.
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Affiliation(s)
- Yi-zhou Shou
- Department of Rehabilitation, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Xin-hua Wang
- Department of Tuina, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Gui-fen Yang
- Department of Rehabilitation, Tongde Hospital of Zhejiang Province, Hangzhou, China
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Arpaia P, Coyle D, Esposito A, Natalizio A, Parvis M, Pesola M, Vallefuoco E. Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System. SENSORS (BASEL, SWITZERLAND) 2023; 23:5836. [PMID: 37447686 DOI: 10.3390/s23135836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/08/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
The present study introduces a brain-computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a "neurofeedback" group, which performed motor imagery while receiving feedback, and a "control" group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual's ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.
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Affiliation(s)
- Pasquale Arpaia
- Department of Electrical Engineering and Information Technology (DIETI), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
- Centro Interdipartimentale di Ricerca in Management Sanitario e Innovazione in Sanità (CIRMIS), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
| | - Damien Coyle
- Institute for the Augmented Human, University of Bath, Bath BA2 7AY, UK
- Intelligent Systems Research Centre, University of Ulster, Derry BT48 7JL, UK
| | - Antonio Esposito
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
| | - Angela Natalizio
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Turin, Italy
| | - Marco Parvis
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Turin, Italy
| | - Marisa Pesola
- Department of Electrical Engineering and Information Technology (DIETI), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
| | - Ersilia Vallefuoco
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Università Degli Studi di Napoli Federico II, 80125 Naples, Italy
- Department of Psychology and Cognitive Science, University of Trento, 38122 Rovereto, Italy
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12
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Nieto-Escamez F, Cortés-Pérez I, Obrero-Gaitán E, Fusco A. Virtual Reality Applications in Neurorehabilitation: Current Panorama and Challenges. Brain Sci 2023; 13:brainsci13050819. [PMID: 37239291 DOI: 10.3390/brainsci13050819] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023] Open
Abstract
Central Nervous System Diseases are a leading cause of disability worldwide, posing significant social and economic burdens for patients, their families, caregivers, and society as a whole [...].
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Affiliation(s)
- Francisco Nieto-Escamez
- Department of Psychology, University of Almeria, 04120 Almeria, Spain
- Center for Neuropsychological Assessment and Rehabilitation (CERNEP), 04120 Almeria, Spain
| | - Irene Cortés-Pérez
- Department of Health Sciences, University of Jaen, Paraje Las Lagunillas s/n, 23071 Jaen, Spain
| | - Esteban Obrero-Gaitán
- Department of Health Sciences, University of Jaen, Paraje Las Lagunillas s/n, 23071 Jaen, Spain
| | - Augusto Fusco
- UOC Neuroriabilitazione ad Alta Intensità, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
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13
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Ren Y, Lin C, Zhou Q, Yingyuan Z, Wang G, Lu A. Effectiveness of virtual reality games in improving physical function, balance and reducing falls in balance-impaired older adults: A systematic review and meta-analysis. Arch Gerontol Geriatr 2023; 108:104924. [PMID: 36680968 DOI: 10.1016/j.archger.2023.104924] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/05/2022] [Accepted: 01/04/2023] [Indexed: 01/07/2023]
Abstract
BACKGROUND In recent years, sports games based on virtual reality (VR) have been widely used in the prevention and treatment of diseases related to the elderly. However, there seems to be no consensus on the improvement and comparison of physical function, balance and falls in elderly people with balance impairment. OBJECTIVE This study aims to explore the effects of VR intervention on physical function, balance and falls in elderly people with balance impairment. METHODS Systematic literature searches of the PubMed, Web of Science, Elsevier, Cochrane, CNKI, and Wanfang databases were performed for VR games-related randomized controlled trials or comparison studies among elderly participants with impaired balance, published in English or Chinese until March 20, 2022. The Cochrane collaboration risk of bias tool was used to evaluate the methodological quality of the studies. A meta-analysis was performed to calculate the standardized mean deviation or mean difference of the sample and its 95% confidence interval (CI) in VR games. RESULTS The systematic review included 23 studies. The results showed that VR intervention had significant effects on hand grip strength (MD:1.30, P = 0.040), knee extension strength (MD:-6.27, P<0.001), five times sit-to-stand test scores (MD:1.13, P = 0.030), timed up-and-go test scores (MD:-1.01, P = 0.001), berg balance scale scores (MD:2.37, P<0.001), and falls efficacy scale scores (SMD:-0.28, P = 0.020). Subgroup analysis results showed that VR intervention was more effective on improving TUG and BBS scores than the conventional exercise group (MD=-0.54, P = 0.004; MD=3.24, P<0.001) and the non-intervention group (MD=-0.98, P = 0.001; MD=3.30, P < 0.001). The balance training-based VR had a significant effect on improving TUG (MD=-1.03, P = 0.004) and BBS (MD=2.93, P<0.001), and 20-45 min intervention, ≥3 times/wk, 5-8 wk cycles were significant in improving TUG (MD=-0.89, P<0.001; MD=-0.75, P = 0.0003; MD=-1.54, P<0.0001). VR intervention significantly improved TUG (MD=-2.27, P<0.0001) and BBS (MD=3.41, P<0.0001) in older adults in the hospital or nursing home compared with those residing in communities. CONCLUSION VR interventions can help the elderly with impaired balance to overcome traditional sports obstacles and improve physical function, balance and minimize falls. Balance training-based VR intervention is more effective in balance recovery and fall prevention compared with game program. An intervention plan comprising 20-45 min, 5-8 wk cycles, and ≥3 times/wk frequency has significantly higher effects for high-risk elderly populations living in hospitals or nursing homes.
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Affiliation(s)
- Yuanyuan Ren
- Physical Education and sport science, Soochow University, Suzhou, Jiangsu Province, China
| | - Chenli Lin
- Physical Education and sport science, Soochow University, Suzhou, Jiangsu Province, China
| | - Qin Zhou
- Physical Education and sport science, Soochow University, Suzhou, Jiangsu Province, China
| | - Zhang Yingyuan
- Hebei Normal University of Science & Technology, Hebei, Hebei Province, China
| | - Guodong Wang
- Physical Education and sport science, Soochow University, Suzhou, Jiangsu Province, China.
| | - Aming Lu
- Physical Education and sport science, Soochow University, Suzhou, Jiangsu Province, China
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14
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Sznajder J, Barć K, Kuźma-Kozakiewicz M. Low intensity exercise training in patients with amyotrophic lateral sclerosis - factors influencing eligibility and compliance to the clinical trial. Neurol Res 2023:1-9. [PMID: 36976932 DOI: 10.1080/01616412.2023.2194184] [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: 03/30/2023]
Abstract
BACKGROUND To date, there are no evidence-based recommendations for physical therapy in amyotrophic lateral sclerosis (ALS). The reason is a low number of related clinical trials (CTs), restricted sample sizes and a high dropout rate. It may influence the profile of the participants, while the final results might not translate to the general ALS population. OBJECTIVE To analyze factors affecting the ALS patients' enrollment and retention to the study, and to describe a profile of participants as compared to the eligible group. METHODS A total of 104 ALS patients were offered participation in a CT of low-intensity exercises at home. Forty-six patients were recruited. Demographic and clinical data (El Escorial criteria, site of onset, diagnosis delay, disease duration, amyotrophic lateral sclerosis functional rating scale - revised [ALSFRS-R], Medical Research Council [MRC], hand-held dynamometry) were analyzed every 3 months. RESULTS Male gender, younger age and a higher ALSFRS predicted enrollment, while male gender, higher ALSFRS-R and MRC predicted retention in the study. A long commute to the study site and a fast disease progression were the main reasons influencing both enrollment and retention. Despite a high dropout rate, study participants were representative for the general ALS population. CONCLUSION The above demographic, clinical and logistic factors need to be considered when designing studies in ALS population.
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Affiliation(s)
- J Sznajder
- Department of Rehabilitation, Józef Piłsudski University of Physical Education in Warsaw, Warsaw, Poland
- Department of Neurology, University Clinical Centre of Medical University of Warsaw, Warsaw, Poland
| | - K Barć
- Department of Neurology, University Clinical Centre of Medical University of Warsaw, Warsaw, Poland
| | - M Kuźma-Kozakiewicz
- Department of Neurology, University Clinical Centre of Medical University of Warsaw, Warsaw, Poland
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
- Neurodegenerative Diseases Research Group, Medical University of Warsaw, Warsaw, Poland
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15
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McLean E, Cornwell MA, Bender HA, Sacks-Zimmerman A, Mandelbaum S, Koay JM, Raja N, Kohn A, Meli G, Spat-Lemus J. Innovations in Neuropsychology: Future Applications in Neurosurgical Patient Care. World Neurosurg 2023; 170:286-295. [PMID: 36782427 DOI: 10.1016/j.wneu.2022.09.103] [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: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 02/11/2023]
Abstract
Over the last century, collaboration between clinical neuropsychologists and neurosurgeons has advanced the state of the science in both disciplines. These advances have provided the field of neuropsychology with many opportunities for innovation in the care of patients prior to, during, and following neurosurgical intervention. Beyond giving a general overview of how present-day advances in technology are being applied in the practice of neuropsychology within a neurological surgery department, this article outlines new developments that are currently unfolding. Improvements in remote platform, computer interface, "real-time" analytics, mobile devices, and immersive virtual reality have the capacity to increase the customization, precision, and accessibility of neuropsychological services. In doing so, such innovations have the potential to improve outcomes and ameliorate health care disparities.
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Affiliation(s)
- Erin McLean
- Department of Psychology, Hofstra University, Hempstead, New York, USA; Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA
| | - Melinda A Cornwell
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA
| | - H Allison Bender
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA.
| | | | - Sarah Mandelbaum
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA; Department of Clinical Psychology with Health Emphasis, Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, New York, USA
| | - Jun Min Koay
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA; Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida, USA
| | - Noreen Raja
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA; Graduate School of Applied and Professional Psychology, Rutgers University, Piscataway, New Jersey, USA
| | - Aviva Kohn
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA; Department of Clinical Psychology with Health Emphasis, Ferkauf Graduate School of Psychology, Yeshiva University, Bronx, New York, USA
| | - Gabrielle Meli
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA; Department of Human Ecology, Cornell University, Ithaca, New York, USA
| | - Jessica Spat-Lemus
- Department of Neurological Surgery, Weill Cornell Medicine, New York, New York, USA
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16
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Identifying Thematics in a Brain-Computer Interface Research. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:2793211. [PMID: 36643889 PMCID: PMC9833923 DOI: 10.1155/2023/2793211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 01/05/2023]
Abstract
This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.
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17
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Leemhuis E, Favieri F, Forte G, Pazzaglia M. Integrated Neuroregenerative Techniques for Plasticity of the Injured Spinal Cord. Biomedicines 2022; 10:biomedicines10102563. [PMID: 36289825 PMCID: PMC9599452 DOI: 10.3390/biomedicines10102563] [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: 08/15/2022] [Revised: 09/18/2022] [Accepted: 10/10/2022] [Indexed: 11/16/2022] Open
Abstract
On the slow path to improving the life expectancy and quality of life of patients post spinal cord injury (SCI), recovery remains controversial. The potential role of the regenerative capacity of the nervous system has led to numerous attempts to stimulate the SCI to re-establish the interrupted sensorimotor loop and to understand its potential in the recovery process. Numerous resources are now available, from pharmacological to biomolecular approaches and from neuromodulation to sensorimotor rehabilitation interventions based on the use of various neural interfaces, exoskeletons, and virtual reality applications. The integration of existing resources seems to be a promising field of research, especially from the perspective of improving living conditions in the short to medium term. Goals such as reducing chronic forms of neuropathic pain, regaining control over certain physiological activities, and enhancing residual abilities are often more urgent than complete functional recovery. In this perspective article, we provide an overview of the latest interventions for the treatment of SCI through broad phases of injury rehabilitation. The underlying intention of this work is to introduce a spinal cord neuroplasticity-based multimodal approach to promote functional recovery and improve quality of life after SCI. Nonetheless, when used separately, biomolecular therapeutic approaches have been shown to have modest outcomes.
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Affiliation(s)
- Erik Leemhuis
- Dipartimento di Psicologia, Sapienza Università di Roma, 00185 Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Correspondence: (E.L.); (M.P.)
| | - Francesca Favieri
- Dipartimento di Psicologia, Sapienza Università di Roma, 00185 Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Giuseppe Forte
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Dipartimento di Psicologia Dinamica, Clinica e Salute, Sapienza Università di Roma, 00185 Roma, Italy
| | - Mariella Pazzaglia
- Dipartimento di Psicologia, Sapienza Università di Roma, 00185 Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Correspondence: (E.L.); (M.P.)
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18
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Behboodi A, Lee WA, Hinchberger VS, Damiano DL. Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review. J Neuroeng Rehabil 2022; 19:104. [PMID: 36171602 PMCID: PMC9516814 DOI: 10.1186/s12984-022-01081-9] [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: 03/25/2022] [Accepted: 09/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background Brain–computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications? Methods We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes. Results From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n = 13) and the Action Research Arm Test (ARAT; n = 6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose. Conclusion This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.
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Affiliation(s)
- Ahad Behboodi
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | - Walker A Lee
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | | | - Diane L Damiano
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA.
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19
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Mane R, Wu Z, Wang D. Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: a perspective review. Stroke Vasc Neurol 2022; 7:svn-2022-001506. [PMID: 35853669 PMCID: PMC9811566 DOI: 10.1136/svn-2022-001506] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 06/17/2022] [Indexed: 01/17/2023] Open
Abstract
Brain-computer interface (BCI) technology translates brain activity into meaningful commands to establish a direct connection between the brain and the external world. Neuroscientific research in the past two decades has indicated a tremendous potential of BCI systems for the rehabilitation of patients suffering from poststroke impairments. By promoting the neuronal recovery of the damaged brain networks, BCI systems have achieved promising results for the recovery of poststroke motor, cognitive, and language impairments. Also, several assistive BCI systems that provide alternative means of communication and control to severely paralysed patients have been proposed to enhance patients' quality of life. In this article, we present a perspective review of the recent advances and challenges in the BCI systems used in the poststroke rehabilitation of motor, cognitive, and communication impairments.
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Affiliation(s)
| | | | - David Wang
- Neurovascular Division, Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
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20
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Le Franc S, Herrera Altamira G, Guillen M, Butet S, Fleck S, Lécuyer A, Bougrain L, Bonan I. Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives. Front Hum Neurosci 2022; 16:917909. [PMID: 35911589 PMCID: PMC9332194 DOI: 10.3389/fnhum.2022.917909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/20/2022] [Indexed: 11/28/2022] Open
Abstract
Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.
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Affiliation(s)
- Salomé Le Franc
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- *Correspondence: Salomé Le Franc,
| | | | - Maud Guillen
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- Neurology Unit, University Hospital of Rennes, Rennes, France
| | - Simon Butet
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Stéphanie Fleck
- Université de Lorraine, CNRS, LORIA, Nancy, France
- EA7312 Laboratoire de Psychologie Ergonomique et Sociale pour l’Expérience Utilisateurs (PERSEUS), Metz, France
| | - Anatole Lécuyer
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | | | - Isabelle Bonan
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
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21
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Wen D, Li R, Tang H, Liu Y, Wan X, Dong X, Saripan MI, Lan X, Song H, Zhou Y. Task-state EEG signal classification for spatial cognitive evaluation based on multi-scale high-density convolutional neural network. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1041-1051. [PMID: 35404820 DOI: 10.1109/tnsre.2022.3166224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. And the coupling features under the combination of multi-frequency bands were transformed into multi-spectral images. At the same time, the idea of Densenet was introduced to improve the multi-scale convolutional neural network. Firstly, according to the discreteness of multispectral EEG image features, two-scale convolution kernels were used to calculate and learn useful channel and frequency band feature information in multispectral image data. Secondly, to enhance feature propagation and reduce the number of parameters, the dense network was connected after the multi-scale convolutional network, and the learning rate change function of the stochastic gradient descent algorithm was optimized to objectively evaluate the training effect. The experimental results showed that compared with the classical convolution neural network (CNN) and multi-scale convolution neural network, the proposed MHCNN had better classification performance in the six frequency band combinations with the highest accuracy of 98%: Theta-Alpha2-Gamma, Alpha2-Beta2-Gamma, Beta1-Beta2-Gamma, Theta-Beta2-Gamma, Theta-Alpha1-Gamma, and Alpha1-Alpha2-Gamma. By comparing the classification results of six frequency band combinations, it was found that the combination of the Theta-Beta2-Gamma band had the best classification effect. The MHCNN classification method proposed in this research could be used as an effective biological indicator of spatial cognitive training effect and could be extended to other brain function evaluations.
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22
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Wen D, Xu J, Wu Z, Liu Y, Zhou Y, Li J, Wang S, Dong X, Saripan MI, Song H. The Effective Cognitive Assessment and Training Methods for COVID-19 Patients With Cognitive Impairment. Front Aging Neurosci 2022; 13:827273. [PMID: 35087399 PMCID: PMC8787269 DOI: 10.3389/fnagi.2021.827273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Dong Wen
- Brain Computer Intelligence and Intelligent Health Institution, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Jian Xu
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Zhonglin Wu
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yijun Liu
- Department of Statistics, School of Science, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- Department of Computer Science and Technology, School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
- *Correspondence: Yanhong Zhou
| | - Jingjing Li
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Shaochang Wang
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xianlin Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - M. Iqbal Saripan
- Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
| | - Haiqing Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Haiqing Song
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Camargo-Vargas D, Callejas-Cuervo M, Mazzoleni S. Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:4312. [PMID: 34202546 PMCID: PMC8271710 DOI: 10.3390/s21134312] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/09/2021] [Accepted: 06/18/2021] [Indexed: 12/22/2022]
Abstract
In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.
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Affiliation(s)
- Daniela Camargo-Vargas
- Software Research Group, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia;
| | - Mauro Callejas-Cuervo
- School of Computer Science, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia
| | - Stefano Mazzoleni
- Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy;
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Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9967348. [PMID: 34239936 PMCID: PMC8235968 DOI: 10.1155/2021/9967348] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/04/2021] [Indexed: 11/17/2022]
Abstract
With the continuous development of artificial intelligence technology, "brain-computer interfaces" are gradually entering the field of medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries' strategic plans for innovating this field, and subsequently, major funding and talent have been invested in this technology. In neurological rehabilitation for stroke patients, the use of BCIs opens up a new chapter in "top-down" rehabilitation. In our study, we first reviewed the latest BCI technologies, then presented recent research advances and landmark findings in BCI-based neurorehabilitation for stroke patients. Neurorehabilitation was focused on the areas of motor, sensory, speech, cognitive, and environmental interactions. Finally, we summarized the shortcomings of BCI use in the field of stroke neurorehabilitation and the prospects for BCI technology development for rehabilitation.
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Xue X, Tu H, Deng Z, Zhou L, Li N, Wang X. Effects of brain-computer interface training on upper limb function recovery in stroke patients: A protocol for systematic review and meta-analysis. Medicine (Baltimore) 2021; 100:e26254. [PMID: 34115016 PMCID: PMC8202595 DOI: 10.1097/md.0000000000026254] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND In recent years, with the development of medical technology and the increase of inter-disciplinary cooperation technology, new methods in the field of artificial intelligence medicine emerge in an endless stream. Brain-computer interface (BCI), as a frontier technology of multidisciplinary integration, has been widely used in various fields. Studies have shown that BCI-assisted training can improve upper limb function in stroke patients, but its effect is still controversial and lacks evidence-based evidence, which requires further exploration and confirmation. Therefore, the main purpose of this paper is to systematically evaluate the efficacy of different BCI-assisted training on upper limb function recovery in stroke patients, to provide a reference for the application of BCI-assisted technology in stroke rehabilitation. METHODS We will search PubMed, Web of Science, The Cochrane Library, Chinese National Knowledge Infrastructure Database, Wanfang Data, Weipu Electronics, and other databases (from the establishment to February 2021) for full text in Chinese and English. Randomized controlled trials were collected to examine the effect of BCI-assisted training on upper limb functional recovery in stroke patients. We will consider inclusion, select high-quality articles for data extraction and analysis, and summarize the intervention effect of BCI-assisted training on the upper limb function of stroke patients. Two reviewers will screen titles, abstracts, and full texts independently according to inclusion criteria; Data extraction and risk of bias assessment were performed in the included studies. We will use a hierarchy of recommended assessment, development, and assessment methods to assess the overall certainty of the evidence and report findings accordingly. Endnote X8 will be applied in selecting the study, Review Manager 5.3 will be applied in analyzing and synthesizing. RESULTS The results will provide evidence for judging whether BCI is effective and safe in improving upper limb function in patients with stroke. CONCLUSION Our study will provide reliable evidence for the effect of BCI technology on the improvement of upper limb function in stroke patients. PROSPERO REGISTRATION NUMBER CRD42021250378.
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Affiliation(s)
- Xiali Xue
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Huan Tu
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Zhongyi Deng
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Ling Zhou
- School of Sports Medicine and Health, Chengdu Sport University, Chengdu, Sichuan Province
| | - Ning Li
- Institute of Sports Medicine and Health, Chengdu Sport University
| | - Xiaokun Wang
- The People's Hospital of Mancheng District, Baoding, Hebei Province, China
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Systematic review of training environments with motor imagery brain–computer interface: Coherent taxonomy, open issues and recommendation pathway solution. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00560-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Borisova V, Isakova E, Kotov S. Cognitive rehabilitation after stroke using non-pharmacological approaches. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:26-32. [DOI: 10.17116/jnevro202112112226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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