José M. Azorín

3.0k total citations
162 papers, 2.0k citations indexed

About

José M. Azorín is a scholar working on Cognitive Neuroscience, Biomedical Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, José M. Azorín has authored 162 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 111 papers in Cognitive Neuroscience, 72 papers in Biomedical Engineering and 66 papers in Cellular and Molecular Neuroscience. Recurrent topics in José M. Azorín's work include EEG and Brain-Computer Interfaces (106 papers), Neuroscience and Neural Engineering (66 papers) and Muscle activation and electromyography studies (58 papers). José M. Azorín is often cited by papers focused on EEG and Brain-Computer Interfaces (106 papers), Neuroscience and Neural Engineering (66 papers) and Muscle activation and electromyography studies (58 papers). José M. Azorín collaborates with scholars based in Spain, Mexico and United States. José M. Azorín's co-authors include Eduardo Iáñez, Andrés Úbeda, Enrique Hortal, Mario Ortíz, Eduardo Fernández, Álvaro Costa, José L. Contreras-Vidal, Rafaél Aracil, José María Sabater-Navarro and Carlos Pérez-Vidal and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Automatica.

In The Last Decade

José M. Azorín

149 papers receiving 2.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
José M. Azorín Spain 24 1.4k 736 680 484 217 162 2.0k
Jörn Vogel Germany 14 1.7k 1.2× 781 1.1× 1.4k 2.0× 263 0.5× 73 0.3× 33 2.2k
Panagiotis Artemiadis United States 29 1.5k 1.1× 2.2k 3.0× 598 0.9× 296 0.6× 511 2.4× 111 2.9k
Kianoush Nazarpour United Kingdom 27 1.4k 1.0× 1.7k 2.4× 819 1.2× 274 0.6× 113 0.5× 157 2.5k
S.P. Levine United States 26 1.4k 1.0× 259 0.4× 681 1.0× 777 1.6× 115 0.5× 65 2.5k
Luzheng Bi China 23 1.3k 0.9× 374 0.5× 575 0.8× 483 1.0× 79 0.4× 98 1.8k
Strahinja Došen Denmark 37 2.6k 1.9× 3.1k 4.2× 1.8k 2.6× 388 0.8× 321 1.5× 165 4.1k
Axel Gräser Germany 21 1.4k 1.0× 184 0.3× 884 1.3× 486 1.0× 60 0.3× 63 1.9k
Noman Naseer Pakistan 24 2.0k 1.5× 1.5k 2.1× 479 0.7× 220 0.5× 72 0.3× 105 3.1k
Alexandre Campeau‐Lecours Canada 23 682 0.5× 1.4k 1.8× 227 0.3× 559 1.2× 194 0.9× 79 2.1k
Simone Benatti Italy 25 890 0.6× 1.3k 1.7× 346 0.5× 444 0.9× 47 0.2× 98 2.3k

Countries citing papers authored by José M. Azorín

Since Specialization
Citations

This map shows the geographic impact of José M. Azorín's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by José M. Azorín with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José M. Azorín more than expected).

Fields of papers citing papers by José M. Azorín

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José M. Azorín. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by José M. Azorín. The network helps show where José M. Azorín may publish in the future.

Co-authorship network of co-authors of José M. Azorín

This figure shows the co-authorship network connecting the top 25 collaborators of José M. Azorín. A scholar is included among the top collaborators of José M. Azorín based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with José M. Azorín. José M. Azorín is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ferrero, Laura, et al.. (2024). Brain–machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study. Journal of NeuroEngineering and Rehabilitation. 21(1). 48–48. 8 indexed citations
2.
Ortíz, Mario, et al.. (2024). Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential. Computer Methods and Programs in Biomedicine. 255. 108332–108332. 2 indexed citations
3.
4.
Ferrero, Laura, et al.. (2023). Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton. iScience. 26(5). 106675–106675. 24 indexed citations
5.
Ferrero, Laura, et al.. (2022). Decoding of Turning Intention during Walking Based on EEG Biomarkers. Biosensors. 12(8). 555–555. 2 indexed citations
6.
Ferrero, Laura, et al.. (2022). Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation. Applied Sciences. 12(1). 415–415. 4 indexed citations
7.
Barría, Patricio, et al.. (2022). Biomechanical Assessment of Post-Stroke Patients’ Upper Limb before and after Rehabilitation Therapy Based on FES and VR. Sensors. 22(7). 2693–2693. 10 indexed citations
8.
Ferrero, Laura, et al.. (2022). Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum. Brain Sciences. 12(2). 248–248. 7 indexed citations
9.
Barría, Patricio, et al.. (2022). EEG Evaluation in a Neuropsychological Intervention Program Based on Virtual Reality in Adults with Parkinson’s Disease. Biosensors. 12(9). 751–751. 3 indexed citations
10.
Barría, Patricio, et al.. (2022). A Robot-Assisted Therapy to Increase Muscle Strength in Hemiplegic Gait Rehabilitation. Frontiers in Neurorobotics. 16. 837494–837494. 13 indexed citations
11.
Ferrero, Laura, et al.. (2021). A BMI Based on Motor Imagery and Attention for Commanding a Lower-Limb Robotic Exoskeleton: A Case Study. Applied Sciences. 11(9). 4106–4106. 22 indexed citations
12.
Ferrero, Laura, et al.. (2021). Brain Symmetry Analysis during the Use of a BCI Based on Motor Imagery for the Control of a Lower-Limb Exoskeleton. Symmetry. 13(9). 1746–1746. 14 indexed citations
13.
Barría, Patricio, et al.. (2021). The Actuation System of the Ankle Exoskeleton T-FLEX: First Use Experimental Validation in People with Stroke. Brain Sciences. 11(4). 412–412. 24 indexed citations
14.
Ferrero, Laura, et al.. (2021). Improving Motor Imagery of Gait on a Brain–Computer Interface by Means of Virtual Reality: A Case of Study. IEEE Access. 9. 49121–49130. 20 indexed citations
15.
He, Yongtian, David Eguren, José M. Azorín, et al.. (2018). Brain–machine interfaces for controlling lower-limb powered robotic systems. Journal of Neural Engineering. 15(2). 21004–21004. 166 indexed citations
16.
Iáñez, Eduardo, et al.. (2017). Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power. Journal of NeuroEngineering and Rehabilitation. 14(1). 31–31. 17 indexed citations
17.
Hortal, Enrique, et al.. (2014). First steps in the development of an EEG-based system to detect intention of gait initiation. 167–171. 5 indexed citations
18.
Úbeda, Andrés, Eduardo Iáñez, José M. Azorín, & Carlos Pérez-Vidal. (2013). Endogenous brain–machine interface based on the correlation of EEG maps. Computer Methods and Programs in Biomedicine. 112(2). 302–308. 5 indexed citations
19.
Iáñez, Eduardo, José M. Azorín, Eduardo Fernández, & Andrés Úbeda. (2010). Interface Based on Electrooculography for Velocity Control of a Robot Arm. Applied Bionics and Biomechanics. 7(3). 199–207. 12 indexed citations
20.
Azorín, José M., José María Sabater-Navarro, Luis Payá, & Nicolás García-Aracil. (2004). Kinematics correspondence & scaling issues in virtual telerobotics systems. World Automation Congress. 15. 383–388. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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