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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial
Alexander Frolov, О. А. Мокиенко, R. Kh. Lyukmanov, et al.
Frontiers in Neuroscience (2017) Vol. 11
Open Access | Times Cited: 321

Showing 26-50 of 321 citing articles:

Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation
Siyu Yang, Ruobing Li, Hongtao Li, et al.
BioMed Research International (2021) Vol. 2021, pp. 1-11
Open Access | Times Cited: 55
EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning
Sergio Pérez-Velasco, Eduardo Santamaría-Vázquez, Víctor Martínez-Cagigal, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022) Vol. 30, pp. 1766-1775
Open Access | Times Cited: 44
Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis
Fangcun Li, Ding Zhang, Jie Chen, et al.
Frontiers in Neuroscience (2023) Vol. 17
Open Access | Times Cited: 25
Highly Interactive Brain–Computer Interface Based on Flicker-Free Steady-State Motion Visual Evoked Potential
Chengcheng Han, Guanghua Xu, Jun Xie, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 67
Functional networks of the brain: from connectivity restoration to dynamic integration
Alexander E. Hramov, Nikita Frolov, В. А. Максименко, et al.
Physics-Uspekhi (2020) Vol. 64, Iss. 6, pp. 584-616
Closed Access | Times Cited: 65
Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation
V. K. Benzy, A. P. Vinod, R Subasree, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2020) Vol. 28, Iss. 12, pp. 3051-3062
Closed Access | Times Cited: 65
Embodiment Is Related to Better Performance on a Brain–Computer Interface in Immersive Virtual Reality: A Pilot Study
Julia M. Juliano, Ryan Spicer, Athanasios Vourvopoulos, et al.
Sensors (2020) Vol. 20, Iss. 4, pp. 1204-1204
Open Access | Times Cited: 64
A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback
Mathis Fleury, Giulia Lioi, Christian Barillot, et al.
Frontiers in Neuroscience (2020) Vol. 14
Open Access | Times Cited: 63
Motor execution reduces EEG signals complexity: Recurrence quantification analysis study
Elena Pitsik, Nikita Frolov, K. Hauke Kraemer, et al.
Chaos An Interdisciplinary Journal of Nonlinear Science (2020) Vol. 30, Iss. 2
Open Access | Times Cited: 60
Brain‐machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review
R. Carvalho, N. S. Dias, João Cerqueira
Physiotherapy Research International (2019) Vol. 24, Iss. 2
Closed Access | Times Cited: 57
Longitudinal Analysis of Stroke Patients’ Brain Rhythms during an Intervention with a Brain-Computer Interface
Ruben I. Carino-Escobar, Paul Carrillo‐Mora, Raquel Valdés-Cristerna, et al.
Neural Plasticity (2019) Vol. 2019, pp. 1-11
Open Access | Times Cited: 55
Brain oscillatory activity as a biomarker of motor recovery in chronic stroke
Andreas M. Ray, Thiago C. Figueiredo, Eduardo López‐Larraz, et al.
Human Brain Mapping (2019) Vol. 41, Iss. 5, pp. 1296-1308
Open Access | Times Cited: 55
Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training
Qiong Wu, Zan Yue, Yunxiang Ge, et al.
Frontiers in Neurology (2020) Vol. 10
Open Access | Times Cited: 55
Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis
Antje Kruse, Zorica Suica, Jan Taeymans, et al.
BMC Neurology (2020) Vol. 20, Iss. 1
Open Access | Times Cited: 54
Cognitive and Affective Brain–Computer Interfaces for Improving Learning Strategies and Enhancing Student Capabilities: A Systematic Literature Review
Nuraini Jamil, Abdelkader Nasreddine Belkacem, Sofía Ouhbi, et al.
IEEE Access (2021) Vol. 9, pp. 134122-134147
Open Access | Times Cited: 50
Bioinspired High-Degrees of Freedom Soft Robotic Glove for Restoring Versatile and Comfortable Manipulation
Dong Hyun Kim, Yechan Lee, Hyung‐Soon Park
Soft Robotics (2021) Vol. 9, Iss. 4, pp. 734-744
Closed Access | Times Cited: 48
Brain–Computer Interfaces in Neurorecovery and Neurorehabilitation
Michael J. Young, David J. Lin, Leigh R. Hochberg
Seminars in Neurology (2021) Vol. 41, Iss. 02, pp. 206-216
Open Access | Times Cited: 47
Benefits of deep learning classification of continuous noninvasive brain–computer interface control
James Stieger, Stephen A. Engel, Daniel Suma, et al.
Journal of Neural Engineering (2021) Vol. 18, Iss. 4, pp. 046082-046082
Open Access | Times Cited: 46
Shallow Convolutional Network Excel for Classifying Motor Imagery EEG in BCI Applications
Daily Milanés Hermosilla, Rafael Trujillo Codorniú, Rene López Baracaldo, et al.
IEEE Access (2021) Vol. 9, pp. 98275-98286
Open Access | Times Cited: 45
Brain–Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis
Ippei Nojima, Hisato Sugata, Hiroki Takeuchi, et al.
Neurorehabilitation and neural repair (2021) Vol. 36, Iss. 2, pp. 83-96
Closed Access | Times Cited: 45
Commercial device-based hand rehabilitation systems for stroke patients: State of the art and future prospects
Bo Sheng, Jianyu Zhao, Yanxin Zhang, et al.
Heliyon (2023) Vol. 9, Iss. 3, pp. e13588-e13588
Open Access | Times Cited: 21
Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial
Xiaolu Liu, Wendong Zhang, Weibo Li, et al.
BMC Neurology (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 21
Wearable upper limb robotics for pervasive health: a review
Chukwuemeka Ochieze, Soroush Zare, Ye Sun
Progress in Biomedical Engineering (2023) Vol. 5, Iss. 3, pp. 032003-032003
Open Access | Times Cited: 20
An Adaptive Brain-Computer Interface to Enhance Motor Recovery After Stroke
Rui Zhang, Chushan Wang, Shenghong He, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2023) Vol. 31, pp. 2268-2278
Open Access | Times Cited: 17
Brain-computer interface combined with mental practice and occupational therapy enhances upper limb motor recovery, activities of daily living, and participation in subacute stroke
Aristela de Freitas Zanona, Daniele Piscitelli, Valquiria Martins Seixas, et al.
Frontiers in Neurology (2023) Vol. 13
Open Access | Times Cited: 16

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