Author

R. O'Keeffe

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 4

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 4 Assay:electrophysiology· 4 Organism:human· 4 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

S. F. Atashzar· 3 shared S. Mehrdad· 3 shared S. Rao· 2 shared S. Y. Shirazi· 2 shared A. Del Vecchio· 1 shared A. Edwards· 1 shared A. M. Johnson· 1 shared D. Farina· 1 shared

Activity by Year

2022-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersBioengineering

S. F. Atashzar

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4 papers

Recent·Most cited

Lower-limb Nonparametric Functional Muscle Network: Test-retest Reliability Analysis

R. O'Keeffe, J. Yang, S. Mehrdad, S. Rao, S. F. Atashzar

This study evaluates the test-retest reliability of non-parametric lower-limb functional muscle networks in healthy subjects for controlled and lightly-controlled tasks.

bioRxiv · Feb 8, 2023Bioengineering

Linear versus Nonlinear Muscle Networks: A Case Study to Decode Hidden Synergistic Patterns During Dynamic Lower-limb Tasks

R. O'Keeffe, V. Rathod, S. Y. Shirazi, S. Mehrdad, A. Edwards, S. Rao, S. F. Atashzar

This paper compares linear and nonlinear muscle networks during dynamic lower-limb tasks, finding that nonlinear networks better capture synergistic patterns in complex tasks like sit-to-stand.

bioRxiv · Jan 15, 2023 · PreprintBioengineering

Perilaryngeal Functional Muscle Network in Patients with Vocal Hyperfunction - A Case Study

R. O'Keeffe, Y. Shirazi, S. Mehrdad, T. Crosby, A. M. Johnson, S. F. Atashzar

This paper evaluates functional perilaryngeal muscle networks in three voice disorder patients, finding altered connectivity and asymmetry compared to healthy controls, suggesting asymmetry as a biomarker for vocal hyperfunction.

bioRxiv · Jan 10, 2023 · PreprintBioengineering

Low-frequency motor cortex EEG predicts four levels of rate of change of force during ankle dorsiflexion

R. O'Keeffe, S. Y. Shirazi, A. Del Vecchio, J. Ibanez, N. Mrachacz-Kersting, R. Bighamian, J. Rizzo, D. Farina, S. F. F. Atashzar

This paper uses EEG signals from the motor cortex to classify four levels of force development rate during ankle dorsiflexion with 82% accuracy using time-frequency features.

bioRxiv · Nov 2, 2022 · PreprintBioengineering