Patrick M. Pilarski

3.2k total citations
80 papers, 1.9k citations indexed

About

Patrick M. Pilarski is a scholar working on Biomedical Engineering, Cognitive Neuroscience and Cellular and Molecular Neuroscience. According to data from OpenAlex, Patrick M. Pilarski has authored 80 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Biomedical Engineering, 30 papers in Cognitive Neuroscience and 22 papers in Cellular and Molecular Neuroscience. Recurrent topics in Patrick M. Pilarski's work include Muscle activation and electromyography studies (34 papers), Neuroscience and Neural Engineering (22 papers) and EEG and Brain-Computer Interfaces (18 papers). Patrick M. Pilarski is often cited by papers focused on Muscle activation and electromyography studies (34 papers), Neuroscience and Neural Engineering (22 papers) and EEG and Brain-Computer Interfaces (18 papers). Patrick M. Pilarski collaborates with scholars based in Canada, United States and Germany. Patrick M. Pilarski's co-authors include Richard S. Sutton, Thomas Degris, Johannes Günther, Klaus Diepold, Hao Shen, Jacqueline S. Hebert, Michael R. Dawson, Sophia Adamia, C. Backhouse and Craig S. Chapman and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and PLoS ONE.

In The Last Decade

Patrick M. Pilarski

73 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Patrick M. Pilarski Canada 23 789 482 340 316 193 80 1.9k
Kyrre Glette Norway 16 720 0.9× 390 0.8× 347 1.0× 127 0.4× 150 0.8× 81 1.4k
Hong Zeng China 18 263 0.3× 590 1.2× 280 0.8× 274 0.9× 82 0.4× 77 1.3k
Yongji Wang China 29 735 0.9× 238 0.5× 317 0.9× 112 0.4× 1.3k 6.5× 281 3.3k
Xiaojun Yu China 21 501 0.6× 706 1.5× 161 0.5× 207 0.7× 98 0.5× 128 1.8k
Tony Pipe United Kingdom 25 717 0.9× 608 1.3× 489 1.4× 196 0.6× 673 3.5× 159 2.4k
Yixiong Chen China 21 450 0.6× 162 0.3× 247 0.7× 64 0.2× 67 0.3× 62 1.7k
Markus Reischl Germany 30 497 0.6× 126 0.3× 240 0.7× 268 0.8× 84 0.4× 186 3.3k
Xingang Zhao China 29 1.4k 1.8× 901 1.9× 189 0.6× 295 0.9× 723 3.7× 212 3.0k
Mustafa Suphi Erden United Kingdom 20 683 0.9× 310 0.6× 124 0.4× 114 0.4× 504 2.6× 83 1.5k
Zhen Zhang China 25 587 0.7× 162 0.3× 257 0.8× 54 0.2× 276 1.4× 121 2.2k

Countries citing papers authored by Patrick M. Pilarski

Since Specialization
Citations

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

Fields of papers citing papers by Patrick M. Pilarski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Patrick M. Pilarski. 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 Patrick M. Pilarski. The network helps show where Patrick M. Pilarski may publish in the future.

Co-authorship network of co-authors of Patrick M. Pilarski

This figure shows the co-authorship network connecting the top 25 collaborators of Patrick M. Pilarski. A scholar is included among the top collaborators of Patrick M. Pilarski 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 Patrick M. Pilarski. Patrick M. Pilarski 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.
Campbell, Evan, Ulysse Côté‐Allard, Patrick M. Pilarski, et al.. (2025). (Un)supervised (Co)adaptation via Incremental Learning for Myoelectric Control: Motivation, Review, and Future Directions. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 33. 3565–3582.
2.
3.
Williams, Heather E., et al.. (2024). A multifaceted suite of metrics for comparative myoelectric prosthesis controller research. PLoS ONE. 19(5). e0291279–e0291279. 1 indexed citations
4.
Williams, Heather E., Jacqueline S. Hebert, Patrick M. Pilarski, & Ahmed W. Shehata. (2023). A Case Series in Position-Aware Myoelectric Prosthesis Control Using Recurrent Convolutional Neural Network Classification with Transfer Learning. PubMed. 19. 1–6. 3 indexed citations
5.
Günther, Johannes, et al.. (2022). Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge. Frontiers in Artificial Intelligence. 5. 826724–826724. 1 indexed citations
6.
Schofield, Jonathon S., et al.. (2021). Embodied Cooperation to Promote Forgiving Interactions With Autonomous Machines. Frontiers in Neurorobotics. 15. 661603–661603. 8 indexed citations
7.
Günther, Johannes, et al.. (2020). Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison. PLoS ONE. 15(12). e0243320–e0243320. 13 indexed citations
8.
Günther, Johannes, et al.. (2019). Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm. Adaptive Agents and Multi-Agents Systems. 1967–1969. 2 indexed citations
9.
Günther, Johannes, et al.. (2019). General Dynamic Neural Networks for explainable PID parameter tuning in control engineering: An extensive comparison.. arXiv (Cornell University).
10.
Chapman, Craig S., et al.. (2019). Characterization of normative angular joint kinematics during two functional upper limb tasks. Gait & Posture. 69. 176–186. 31 indexed citations
11.
Pilarski, Patrick M., et al.. (2018). Characterization of normative hand movements during two functional upper limb tasks. PLoS ONE. 13(6). e0199549–e0199549. 44 indexed citations
12.
Manage, Dammika P., et al.. (2018). Monitoring food pathogens: Novel instrumentation for cassette PCR testing. PLoS ONE. 13(5). e0197100–e0197100. 5 indexed citations
13.
Chapman, Craig S., et al.. (2018). Cluster-based upper body marker models for three-dimensional kinematic analysis: Comparison with an anatomical model and reliability analysis. Journal of Biomechanics. 72. 228–234. 30 indexed citations
14.
Dawson, Michael R., Jacqueline S. Hebert, Craig Sherstan, et al.. (2015). Application of real-time machine learning to myoelectric prosthesis control. Prosthetics and Orthotics International. 40(5). 573–581. 45 indexed citations
15.
Adamia, Sophia, Benjamin Haibe‐Kains, Patrick M. Pilarski, et al.. (2013). A Genome-Wide Aberrant RNA Splicing in Patients with Acute Myeloid Leukemia Identifies Novel Potential Disease Markers and Therapeutic Targets. Clinical Cancer Research. 20(5). 1135–1145. 79 indexed citations
16.
Pilarski, Patrick M. & Richard S. Sutton. (2012). Between Instruction and Reward: Human-Prompted Switching.. National Conference on Artificial Intelligence. 2 indexed citations
17.
18.
Sutton, Richard S., Joseph Modayil, Thomas Degris, et al.. (2011). Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. Adaptive Agents and Multi-Agents Systems. 761–768. 123 indexed citations
19.
Pilarski, Linda M., Patrick M. Pilarski, & Andrew R. Belch. (2010). Multiple myeloma may include microvessel endothelial cells of malignant origin. Leukemia & lymphoma. 51(4). 592–597. 1 indexed citations
20.
Pilarski, Patrick M., Sophia Adamia, & C. Backhouse. (2005). An adaptable microvalving system for on-chip polymerase chain reactions. Journal of Immunological Methods. 305(1). 48–58. 26 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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