Rosa Q. So

959 total citations
40 papers, 631 citations indexed

About

Rosa Q. So is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Electrical and Electronic Engineering. According to data from OpenAlex, Rosa Q. So has authored 40 papers receiving a total of 631 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Cellular and Molecular Neuroscience, 28 papers in Cognitive Neuroscience and 13 papers in Electrical and Electronic Engineering. Recurrent topics in Rosa Q. So's work include Neuroscience and Neural Engineering (29 papers), EEG and Brain-Computer Interfaces (27 papers) and Neural dynamics and brain function (14 papers). Rosa Q. So is often cited by papers focused on Neuroscience and Neural Engineering (29 papers), EEG and Brain-Computer Interfaces (27 papers) and Neural dynamics and brain function (14 papers). Rosa Q. So collaborates with scholars based in Singapore, United States and Canada. Rosa Q. So's co-authors include Warren M. Grill, George C. McConnell, Alexander R. Kent, David T. Brocker, Robert E. Gross, Brandon D. Swan, Dennis A. Turner, Camilo Libedinsky, Kai Keng Ang and Arindam Basu and has published in prestigious journals such as Journal of Neuroscience, Journal of Neurophysiology and Journal of neurosurgery.

In The Last Decade

Rosa Q. So

39 papers receiving 625 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rosa Q. So Singapore 12 477 354 297 97 41 40 631
Brett Wingeier United States 8 543 1.1× 466 1.3× 663 2.2× 39 0.4× 105 2.6× 11 1.1k
Robert Law United States 5 301 0.6× 102 0.3× 631 2.1× 55 0.6× 30 0.7× 6 772
Alan Lai Australia 15 302 0.6× 128 0.4× 477 1.6× 36 0.4× 101 2.5× 38 724
Sheng-Huang Lin Taiwan 12 227 0.5× 347 1.0× 91 0.3× 39 0.4× 89 2.2× 21 527
Omid G. Sani United States 7 180 0.4× 137 0.4× 401 1.4× 33 0.3× 75 1.8× 13 530
Max van den Boom Netherlands 6 270 0.6× 59 0.2× 388 1.3× 99 1.0× 14 0.3× 14 457
Elmar Pels Netherlands 9 348 0.7× 78 0.2× 511 1.7× 126 1.3× 12 0.3× 15 587
Zachary V. Freudenburg Netherlands 14 575 1.2× 100 0.3× 955 3.2× 187 1.9× 15 0.4× 39 1.1k
Abirami Muralidharan United States 10 321 0.7× 257 0.7× 172 0.6× 32 0.3× 28 0.7× 12 391

Countries citing papers authored by Rosa Q. So

Since Specialization
Citations

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

Fields of papers citing papers by Rosa Q. So

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rosa Q. So. 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 Rosa Q. So. The network helps show where Rosa Q. So may publish in the future.

Co-authorship network of co-authors of Rosa Q. So

This figure shows the co-authorship network connecting the top 25 collaborators of Rosa Q. So. A scholar is included among the top collaborators of Rosa Q. So 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 Rosa Q. So. Rosa Q. So 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.
Lo, Yu Tung, et al.. (2022). Neural correlates of learning in a linear discriminant analysis brain-computer interface paradigm. Journal of Neural Engineering. 19(5). 56041–56041.
2.
Wai, Aung Aung Phyo, et al.. (2020). Effects of Stimulus Spatial Resolution on SSVEP Responses under Overt and Covert Attention. PubMed. 11. 3019–3022. 1 indexed citations
3.
4.
Phua, Kok Soon, et al.. (2019). Image-based Motor Imagery EEG Classification using Convolutional Neural Network. 1–4. 11 indexed citations
5.
Zhang, Xiang, Camilo Libedinsky, Rosa Q. So, José C. Prı́ncipe, & Yiwen Wang. (2019). Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 27(9). 1684–1694. 18 indexed citations
6.
So, Rosa Q., et al.. (2019). Real-time Closed Loop Neural Decoding on a Neuromorphic chip. 670–673. 5 indexed citations
7.
8.
9.
Brocker, David T., Brandon D. Swan, Rosa Q. So, et al.. (2017). Optimized temporal pattern of brain stimulation designed by computational evolution. Science Translational Medicine. 9(371). 102 indexed citations
10.
So, Rosa Q., George C. McConnell, & Warren M. Grill. (2016). Frequency-dependent, transient effects of subthalamic nucleus deep brain stimulation on methamphetamine-induced circling and neuronal activity in the hemiparkinsonian rat. Behavioural Brain Research. 320. 119–127. 15 indexed citations
11.
McConnell, George C., Rosa Q. So, & Warren M. Grill. (2016). Failure to suppress low-frequency neuronal oscillatory activity underlies the reduced effectiveness of random patterns of deep brain stimulation. Journal of Neurophysiology. 115(6). 2791–2802. 28 indexed citations
12.
Sammartino, Francesco, et al.. (2015). Neuromodulation for Epilepsy. Neurosurgery Clinics of North America. 27(1). 123–131. 24 indexed citations
13.
Tam, Wing-kin, Rosa Q. So, Cuntai Guan, & Zhi Yang. (2015). EC-PC spike detection for high performance brain-computer interface. PubMed. 9. 5142–5145. 5 indexed citations
14.
So, Rosa Q., Zhiming Xu, Camilo Libedinsky, et al.. (2015). Neural representations of movement intentions during brain-controlled self-motion. 119. 228–231. 6 indexed citations
15.
Ang, Kai Keng, et al.. (2015). Combining firing rate and spike-train synchrony features in the decoding of motor cortical activity. PubMed. 86. 1091–1094. 1 indexed citations
16.
Xu, Zhiming, et al.. (2014). On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity. PubMed. 1. 3049–3052. 10 indexed citations
17.
McConnell, George C., et al.. (2012). Effective Deep Brain Stimulation Suppresses Low-Frequency Network Oscillations in the Basal Ganglia by Regularizing Neural Firing Patterns. Journal of Neuroscience. 32(45). 15657–15668. 142 indexed citations
18.
So, Rosa Q., et al.. (2012). Characterizing Effects of Subthalamic Nucleus Deep Brain Stimulation on Methamphetamine-Induced Circling Behavior in Hemi-Parkinsonian Rats. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 20(5). 626–635. 27 indexed citations
19.
So, Rosa Q., Alexander R. Kent, & Warren M. Grill. (2011). Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study. Journal of Computational Neuroscience. 32(3). 499–519. 106 indexed citations
20.
Lee, S. W. Ricky, et al.. (2004). Multi-chip memory module vvith a flip-chip-on-chip structure and an optional center via hole for underfill dispensing. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 864–869. 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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