Shaun G. Boe

2.3k total citations · 1 hit paper
63 papers, 1.6k citations indexed

About

Shaun G. Boe is a scholar working on Cognitive Neuroscience, Developmental and Educational Psychology and Social Psychology. According to data from OpenAlex, Shaun G. Boe has authored 63 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Cognitive Neuroscience, 25 papers in Developmental and Educational Psychology and 19 papers in Social Psychology. Recurrent topics in Shaun G. Boe's work include Motor Control and Adaptation (35 papers), Sport Psychology and Performance (25 papers) and Action Observation and Synchronization (19 papers). Shaun G. Boe is often cited by papers focused on Motor Control and Adaptation (35 papers), Sport Psychology and Performance (25 papers) and Action Observation and Synchronization (19 papers). Shaun G. Boe collaborates with scholars based in Canada, United States and Austria. Shaun G. Boe's co-authors include Sarah N. Kraeutner, David A. Westwood, Christopher Friesen, Timothy J. Doherty, Daniel W. Stashuk, Diane MacKenzie, Timothy Bardouille, William E. McIlroy, Amanda Marlin and George Mochizuki and has published in prestigious journals such as PLoS ONE, NeuroImage and Scientific Reports.

In The Last Decade

Shaun G. Boe

61 papers receiving 1.6k citations

Hit Papers

Mild Traumatic Brain Injury (mTBI) and chronic cognitive ... 2017 2026 2020 2023 2017 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shaun G. Boe Canada 23 820 362 334 323 320 63 1.6k
Kader Boulanouar France 23 1.4k 1.8× 183 0.5× 525 1.6× 261 0.8× 120 0.4× 38 2.4k
Farsin Hamzei Germany 27 1.2k 1.5× 220 0.6× 390 1.2× 173 0.5× 531 1.7× 48 2.2k
A. M. Gentile United States 17 720 0.9× 297 0.8× 232 0.7× 344 1.1× 263 0.8× 31 1.5k
Christian Marquardt Germany 27 1.2k 1.4× 443 1.2× 386 1.2× 191 0.6× 250 0.8× 53 1.9k
Ingo G. Meister Germany 27 1.8k 2.2× 171 0.5× 286 0.9× 267 0.8× 469 1.5× 52 2.6k
Th. Mulder Netherlands 15 662 0.8× 466 1.3× 92 0.3× 262 0.8× 285 0.9× 28 1.6k
Cheol E. Han South Korea 20 910 1.1× 144 0.4× 190 0.6× 113 0.3× 135 0.4× 50 1.5k
Naznin Virji‐Babul Canada 23 500 0.6× 167 0.5× 219 0.7× 201 0.6× 145 0.5× 78 1.6k
Manuel Dafotakis Germany 24 1.7k 2.1× 440 1.2× 462 1.4× 143 0.4× 334 1.0× 82 3.0k
Donald J. Crammond United States 26 1.4k 1.7× 342 0.9× 1.0k 3.1× 154 0.5× 365 1.1× 114 3.1k

Countries citing papers authored by Shaun G. Boe

Since Specialization
Citations

This map shows the geographic impact of Shaun G. Boe'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 Shaun G. Boe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shaun G. Boe more than expected).

Fields of papers citing papers by Shaun G. Boe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shaun G. Boe. 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 Shaun G. Boe. The network helps show where Shaun G. Boe may publish in the future.

Co-authorship network of co-authors of Shaun G. Boe

This figure shows the co-authorship network connecting the top 25 collaborators of Shaun G. Boe. A scholar is included among the top collaborators of Shaun G. Boe 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 Shaun G. Boe. Shaun G. Boe 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.
Westwood, David A., et al.. (2025). Perceptual and Motor Processes in Motor Imagery. European Journal of Neuroscience. 61(11). e70151–e70151.
2.
Boe, Shaun G., et al.. (2024). Unlike overt movement, motor imagery cannot update internal models. Brain and Cognition. 181. 106219–106219. 1 indexed citations
3.
Kraeutner, Sarah N., et al.. (2024). A kinematically complex multi-articular motor skill for investigating implicit motor learning. Psychological Research. 88(7). 2005–2019.
4.
Rieger, Martina, et al.. (2023). A theoretical perspective on action consequences in action imagery: internal prediction as an essential mechanism to detect errors. Psychological Research. 88(6). 1849–1858. 17 indexed citations
5.
Frank, Cornelia, Sarah N. Kraeutner, Martina Rieger, & Shaun G. Boe. (2023). Learning motor actions via imagery—perceptual or motor learning?. Psychological Research. 88(6). 1820–1832. 24 indexed citations
6.
Edwards, Jodi D., Sandra E. Black, Shaun G. Boe, et al.. (2021). Canadian Platform for Trials in Noninvasive Brain Stimulation (CanStim) Consensus Recommendations for Repetitive Transcranial Magnetic Stimulation in Upper Extremity Motor Stroke Rehabilitation Trials. Neurorehabilitation and neural repair. 35(2). 103–116. 7 indexed citations
8.
Kraeutner, Sarah N., et al.. (2020). Neural and Behavioral Outcomes Differ Following Equivalent Bouts of Motor Imagery or Physical Practice. Journal of Cognitive Neuroscience. 32(8). 1590–1606. 16 indexed citations
9.
Kraeutner, Sarah N., et al.. (2020). Leveraging the effector independent nature of motor imagery when it is paired with physical practice. Scientific Reports. 10(1). 21335–21335. 12 indexed citations
10.
Kraeutner, Sarah N., et al.. (2019). Probing the temporal dynamics of movement inhibition in motor imagery. Brain Research. 1720. 146310–146310. 13 indexed citations
11.
Westwood, David A., et al.. (2018). Movement related sensory feedback is not necessary for learning to execute a motor skill. Behavioural Brain Research. 359. 135–142. 28 indexed citations
12.
Friesen, Christopher, et al.. (2017). Mild Traumatic Brain Injury (mTBI) and chronic cognitive impairment: A scoping review. PLoS ONE. 12(4). e0174847–e0174847. 350 indexed citations breakdown →
13.
Kraeutner, Sarah N., et al.. (2016). Skill acquisition via motor imagery relies on both motor and perceptual learning.. Behavioral Neuroscience. 130(2). 252–260. 42 indexed citations
14.
Boe, Shaun G., et al.. (2014). Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback. NeuroImage. 101. 159–167. 51 indexed citations
15.
Page, Stephen J., et al.. (2014). Modified Constraint-Induced Movement Therapy for Upper Extremity Recovery Post Stroke: What Is the Evidence?. Topics in Stroke Rehabilitation. 21(4). 319–331. 37 indexed citations
16.
Stashuk, Daniel W., et al.. (2009). Probabilistic muscle characterization using QEMG: Application to neuropathic muscle. Muscle & Nerve. 41(1). 18–31. 6 indexed citations
17.
Doherty, Timothy J., Daniel W. Stashuk, & Shaun G. Boe. (2009). Decomposition-enhanced spike triggered averaging MUNE: validity, reliability, and impact of contraction force. Supplements to Clinical neurophysiology. 60. 119–127. 8 indexed citations
18.
Boe, Shaun G., Charles L. Rice, & Timothy J. Doherty. (2008). Estimating Contraction Level Using Root Mean Square Amplitude in Control Subjects and Patients With Neuromuscular Disorders. Archives of Physical Medicine and Rehabilitation. 89(4). 711–718. 28 indexed citations
19.
Boe, Shaun G., Daniel W. Stashuk, & Timothy J. Doherty. (2007). Motor unit number estimates and quantitative motor unit analysis in healthy subjects and patients with amyotrophic lateral sclerosis. Muscle & Nerve. 36(1). 62–70. 33 indexed citations
20.
Boe, Shaun G., Daniel W. Stashuk, William F. Brown, & Timothy J. Doherty. (2004). Decomposition‐based quantitative electromyography: Effect of force on motor unit potentials and motor unit number estimates. Muscle & Nerve. 31(3). 365–373. 66 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026