BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Baniqued PDE, Stanyer EC, Awais M, Alazmani A, Jackson AE, Mon-Williams MA, Mushtaq F, Holt RJ. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J Neuroeng Rehabil 2021;18:15. [PMID: 33485365 DOI: 10.1186/s12984-021-00820-8] [Cited by in Crossref: 33] [Cited by in F6Publishing: 33] [Article Influence: 33.0] [Reference Citation Analysis]
Number Citing Articles
1 Wen L, Xu J, Li D, Pei X, Wang J. Continuous estimation of upper limb joint angle from sEMG based on multiple decomposition feature and BiLSTM network. Biomedical Signal Processing and Control 2023;80:104303. [DOI: 10.1016/j.bspc.2022.104303] [Reference Citation Analysis]
2 Van Caenegem EE, Hamoline G, Waltzing BM, Hardwick RM. Consistent under-reporting of task details in motor imagery research. Neuropsychologia 2022. [DOI: 10.1016/j.neuropsychologia.2022.108425] [Reference Citation Analysis]
3 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 DOI: 10.1186/s12984-022-01081-9] [Reference Citation Analysis]
4 Zhang Y, Liu X, Qiao X, Fan Y. Intellectual Structure and Emerging Trend of Research on rehabilitation robots: A Bibliometric Study (Preprint).. [DOI: 10.2196/preprints.42901] [Reference Citation Analysis]
5 Wu D, Ouyang J, Dai N, Wu M, Tan H, Deng H, Fan Y, Wang D, Jin Z. DeepBrain. Proc ACM Interact Mob Wearable Ubiquitous Technol 2022;6:1-27. [DOI: 10.1145/3550334] [Reference Citation Analysis]
6 Wei Y, Li J, Ji H, Jin L, Liu L, Bai Z, Ye C. A Semi-Supervised Progressive Learning Algorithm for Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2022;30:2067-76. [PMID: 35853068 DOI: 10.1109/TNSRE.2022.3192448] [Reference Citation Analysis]
7 Sidyakina IV, Medsi Group, Clinical Hospital No. 1, Moscow, Russia, Lupanova KV, Korchazhkina NB, Mikhailova AA, Shapovalenko TV, Koneva ES, Medical and Biological University of Innovation and Continuing Education of the Federal Medical Biophysical Center named after A. I. Burnazyan, Medical and Biological University of Innovation and Continuing Education of the Federal Medical Biophysical Center named after A. I. Burnazyan, FSBSI Petrovsky National Research Centre of Surgery, Moscow; FSBEI HE A. I. Yevdokimov Moscow State University of Medicine and Dentistry of the Ministry of Health of Russia, Moscow, FSBSI Petrovsky National Research Centre of Surgery, Moscow; FSBEI HE A. I. Yevdokimov Moscow State University of Medicine and Dentistry of the Ministry of Health of Russia, Moscow, Medsi Group, Clinical Hospital No. 1, Moscow, Russia, Medsi Group, Clinical Hospital No. 1, Moscow, Russia; Sechenov First Moscow State Medical University (Sechenov University), Moscow. Study of the efficacy of comprehensive rehabilitation of fine motor skills in patients after ischemic stroke, using hardware technology with biofeedback. Physiotherapist 2022. [DOI: 10.33920/med-14-2208-03] [Reference Citation Analysis]
8 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. [DOI: 10.3389/fnhum.2022.917909] [Reference Citation Analysis]
9 Vaghei Y, Park EJ, Arzanpour S. Decoding Brain Signals to Classify Gait Direction Anticipation. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022. [DOI: 10.1109/embc48229.2022.9871566] [Reference Citation Analysis]
10 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. [DOI: 10.3389/fnhum.2022.725715] [Reference Citation Analysis]
11 Li X, Tang L, Zhang Z, Wu J, Li Q. Attention and Memory Training System Based on Neural Feedback. 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) 2022. [DOI: 10.1109/cvidliccea56201.2022.9824137] [Reference Citation Analysis]
12 Qu H, Zeng F, Tang Y, Shi B, Wang Z, Chen X, Wang J. The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review. Disabil Rehabil Assist Technol 2022;:1-12. [PMID: 35450498 DOI: 10.1080/17483107.2022.2060354] [Reference Citation Analysis]
13 Zaatri A. Overview of some Command Modes for Human-Robot Interaction Systems. J INFORM SYSTEMS ENG 2022;7:14039. [DOI: 10.55267/iadt.07.12011] [Reference Citation Analysis]
14 Kim B, Ahn K, Nam S, Hyun DJ. Upper extremity exoskeleton system to generate customized therapy motions for stroke survivors. Robotics and Autonomous Systems 2022. [DOI: 10.1016/j.robot.2022.104128] [Reference Citation Analysis]
15 Tedla JS, Gular K, Reddy RS, de Sá Ferreira A, Rodrigues EC, Kakaraparthi VN, Gyer G, Sangadala DR, Qasheesh M, Kovela RK, Nambi G. Effectiveness of Constraint-Induced Movement Therapy (CIMT) on Balance and Functional Mobility in the Stroke Population: A Systematic Review and Meta-Analysis. Healthcare 2022;10:495. [DOI: 10.3390/healthcare10030495] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Yang W, Zhang X, Li Z, Zhang Q, Xue C, Huai Y. The Effect of Brain–Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials. Front Neurosci 2022;15:766879. [DOI: 10.3389/fnins.2021.766879] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
17 Tiboni M, Borboni A, Vérité F, Bregoli C, Amici C. Sensors and Actuation Technologies in Exoskeletons: A Review. Sensors (Basel) 2022;22:884. [PMID: 35161629 DOI: 10.3390/s22030884] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
18 Shen X, Wang X, Lu S, Li Z, Shao W, Wu Y. Research on the real-time control system of lower-limb gait movement based on motor imagery and central pattern generator. Biomedical Signal Processing and Control 2022;71:102803. [DOI: 10.1016/j.bspc.2021.102803] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
19 Hamos J, University of Oradea, Tarca R, Birouas IF, Anton DM, University of Oradea, University of Oradea, University of Oradea. A Review Regarding Neurorehabilitation Technologies for Hand Motor Functions. RM 2022;27:4-8. [DOI: 10.24193/rm.2022.1.1] [Reference Citation Analysis]
20 Gutierrez-Martinez J, Mercado-Gutierrez JA, Carvajal-Gámez BE, Rosas-Trigueros JL, Contreras-Martinez AE. Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions. Front Hum Neurosci 2021;15:772837. [PMID: 34899220 DOI: 10.3389/fnhum.2021.772837] [Reference Citation Analysis]
21 Kina S, Higa H. Brain-Computer Interface System for Hand Rehabilitation. 2021 6th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) 2021. [DOI: 10.1109/iciibms52876.2021.9651564] [Reference Citation Analysis]
22 Marcos-Martínez D, Martínez-Cagigal V, Santamaría-Vázquez E, Pérez-Velasco S, Hornero R. Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population. Entropy (Basel) 2021;23:1574. [PMID: 34945880 DOI: 10.3390/e23121574] [Reference Citation Analysis]
23 Pisla D, Tarnita D, Tucan P, Tohanean N, Vaida C, Geonea ID, Bogdan G, Abrudan C, Carbone G, Plitea N. A Parallel Robot with Torque Monitoring for Brachial Monoparesis Rehabilitation Tasks. Applied Sciences 2021;11:9932. [DOI: 10.3390/app11219932] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
24 Esposito D, Centracchio J, Andreozzi E, Gargiulo GD, Naik GR, Bifulco P. Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey. Sensors (Basel) 2021;21:6863. [PMID: 34696076 DOI: 10.3390/s21206863] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
25 Palumbo A, Gramigna V, Calabrese B, Ielpo N. Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review. Sensors (Basel) 2021;21:6285. [PMID: 34577493 DOI: 10.3390/s21186285] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 10.0] [Reference Citation Analysis]
26 Simon C, Bolton DAE, Kennedy NC, Soekadar SR, Ruddy KL. Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation. Front Neurosci 2021;15:699428. [PMID: 34276299 DOI: 10.3389/fnins.2021.699428] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 11.0] [Reference Citation Analysis]
27 Bobrova EV, Reshetnikova VV, Vershinina EA, Grishin AA, Bobrov PD, Frolov AA, Gerasimenko YP. Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness. Brain Sci 2021;11:853. [PMID: 34202413 DOI: 10.3390/brainsci11070853] [Reference Citation Analysis]
28 Silva EMGS, Holanda LJ, Coutinho GKB, Andrade FS, Nascimento GIS, Nagem DAP, Valentim RAM, Lindquist AR. Effects of Active Upper Limb Orthoses Using Brain-Machine Interfaces for Rehabilitation of Patients With Neurological Disorders: Protocol for a Systematic Review and Meta-Analysis. Front Neurosci 2021;15:661494. [PMID: 34248477 DOI: 10.3389/fnins.2021.661494] [Reference Citation Analysis]
29 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 DOI: 10.1097/MD.0000000000026254] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
30 Righi M, Magrini M, Dolciotti C, Moroni D. A System for Neuromotor Based Rehabilitation on a Passive Robotic Aid. Sensors (Basel) 2021;21:3130. [PMID: 33946361 DOI: 10.3390/s21093130] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
31 Young MJ, Lin DJ, Hochberg LR. Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation. Semin Neurol 2021;41:206-16. [PMID: 33742433 DOI: 10.1055/s-0041-1725137] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]