Virginia Woods

504 total citations
10 papers, 183 citations indexed

About

Virginia Woods is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Biomedical Engineering. According to data from OpenAlex, Virginia Woods has authored 10 papers receiving a total of 183 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cellular and Molecular Neuroscience, 5 papers in Cognitive Neuroscience and 4 papers in Biomedical Engineering. Recurrent topics in Virginia Woods's work include Neuroscience and Neural Engineering (9 papers), EEG and Brain-Computer Interfaces (3 papers) and Muscle activation and electromyography studies (3 papers). Virginia Woods is often cited by papers focused on Neuroscience and Neural Engineering (9 papers), EEG and Brain-Computer Interfaces (3 papers) and Muscle activation and electromyography studies (3 papers). Virginia Woods collaborates with scholars based in United States, United Kingdom and Switzerland. Virginia Woods's co-authors include Jonathan Viventi, Iasonas F. Triantis, C. Toumazou, Michael Trumpis, Chia‐Han Chiang, Robert C. Froemke, Charles Wang, Konstantin Nikolić, Michele N. Insanally and Brinnae Bent and has published in prestigious journals such as IEEE Transactions on Biomedical Engineering, Journal of Neural Engineering and IEEE Solid-State Circuits Letters.

In The Last Decade

Virginia Woods

10 papers receiving 179 citations

Peers

Virginia Woods

CMN

CN

EEE

BE

PP

Balázs Dombovári Hungary

Karen E. Schroeder United States

Chrono S. Nu United States

Song Luan United Kingdom

Qun Cheng United States

Leila Ghanbari United States

Alex K. Vaskov United States

Autumn J. Bullard United States

Devapratim Sarma United States

Kimberly L. Weldy United States

Balázs Dombovári Hungary

Virginia Woods

159 ×1.1

CMN

124 ×1.5

CN

58 ×0.9

EEE

29 ×0.5

BE

11 ×0.8

PP

Citations per year, relative to Virginia Woods Virginia Woods (= 1×) peers Balázs Dombovári

Countries citing papers authored by Virginia Woods

Since Specialization

Citations

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

Fields of papers citing papers by Virginia Woods

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Virginia Woods. 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 Virginia Woods. The network helps show where Virginia Woods may publish in the future.

Co-authorship network of co-authors of Virginia Woods

This figure shows the co-authorship network connecting the top 25 collaborators of Virginia Woods. A scholar is included among the top collaborators of Virginia Woods 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 Virginia Woods. Virginia Woods is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Woods, Virginia, et al.. (2023). A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC. IEEE Solid-State Circuits Letters. 6. 21–24. 10 indexed citations

2.

Woods, Virginia, et al.. (2022). A 16-Channel 60µW Neural Synchrony Processor for Multi-Mode Phase-Locked Neurostimulation. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–2. 10 indexed citations

3.

Woods, Virginia, Michael Trumpis, Brinnae Bent, et al.. (2018). Long-term recording reliability of liquid crystal polymer µECoG arrays. Journal of Neural Engineering. 15(6). 66024–66024. 48 indexed citations

4.

Woods, Virginia, et al.. (2016). In vitro assessment of long-term reliability of low-cost μΕCoG arrays. PubMed. 2016. 4503–4506. 10 indexed citations

5.

Insanally, Michele N., Michael Trumpis, Charles Wang, et al.. (2016). A low-cost, multiplexedμECoG system for high-density recordings in freely moving rodents. Journal of Neural Engineering. 13(2). 26030–26030. 31 indexed citations

6.

Woods, Virginia, Charles Wang, Silvia Bossi, et al.. (2015). A low-cost, 61-channel µECoG array for use in rodents. ENEA Open Archive (National Agency for New Technologies, Energy and Sustainable Economic Development). 573–576. 8 indexed citations

7.

Woods, Virginia, et al.. (2014). Breast cancer detection using high-density flexible electrode arrays and electrical impedance tomography. PubMed. 2014. 1131–1134. 13 indexed citations

8.

Triantis, Iasonas F., et al.. (2012). A Simulation Study of the Combined Thermoelectric Extracellular Stimulation of the Sciatic Nerve of the Xenopus Laevis: The Localized Transient Heat Block. IEEE Transactions on Biomedical Engineering. 59(6). 1758–1769. 43 indexed citations

9.

Woods, Virginia, et al.. (2011). “Capacitive” pulse shapes for platinum cuff electrodes. PubMed. 4. 5408–5411. 1 indexed citations

10.

Woods, Virginia, Iasonas F. Triantis, & C. Toumazou. (2011). Offset prediction for charge-balanced stimulus waveforms. Journal of Neural Engineering. 8(4). 46032–46032. 9 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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