Masa-aki Sato

4.7k total citations · 1 hit paper
113 papers, 3.2k citations indexed

About

Masa-aki Sato is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Masa-aki Sato has authored 113 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Cognitive Neuroscience, 28 papers in Artificial Intelligence and 17 papers in Electrical and Electronic Engineering. Recurrent topics in Masa-aki Sato's work include Neural dynamics and brain function (20 papers), EEG and Brain-Computer Interfaces (16 papers) and Neural Networks and Applications (15 papers). Masa-aki Sato is often cited by papers focused on Neural dynamics and brain function (20 papers), EEG and Brain-Computer Interfaces (16 papers) and Neural Networks and Applications (15 papers). Masa-aki Sato collaborates with scholars based in Japan, United States and Germany. Masa-aki Sato's co-authors include Shin Ishii, Okito Yamashita, Yukiyasu Kamitani, Taku Yoshioka, Ichiro Takemasa, Kenichi Matsubara, Morito Monden, Shigeyuki Oba, Mitsuo Kawato and Frank Tong and has published in prestigious journals such as Physical Review Letters, Neuron and Bioinformatics.

In The Last Decade

Masa-aki Sato

103 papers receiving 3.0k citations

Hit Papers

A Bayesian missing value estimation method for gene expre... 2003 2026 2010 2018 2003 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Masa-aki Sato Japan 25 1.2k 819 489 356 349 113 3.2k
Alexander Kraskov United Kingdom 30 3.5k 3.0× 999 1.2× 461 0.9× 532 1.5× 365 1.0× 45 6.6k
Harald Stögbauer Germany 5 623 0.5× 846 1.0× 175 0.4× 380 1.1× 307 0.9× 6 3.0k
Biswa Sengupta United Kingdom 23 879 0.8× 991 1.2× 182 0.4× 241 0.7× 983 2.8× 36 4.1k
Mark D. McDonnell Australia 24 1.6k 1.4× 497 0.6× 275 0.6× 629 1.8× 187 0.5× 113 4.3k
Michalis Zervakis Greece 23 1.4k 1.2× 277 0.3× 220 0.4× 187 0.5× 769 2.2× 160 3.6k
Sara A. Solla United States 36 1.9k 1.6× 2.3k 2.7× 459 0.9× 284 0.8× 976 2.8× 77 5.5k
Don H. Johnson United States 29 1.4k 1.2× 542 0.7× 764 1.6× 375 1.1× 353 1.0× 162 5.4k
Nathan Intrator Israel 36 1.2k 1.0× 1.0k 1.3× 162 0.3× 251 0.7× 696 2.0× 135 4.5k
Ning Qian United States 28 3.0k 2.6× 709 0.9× 226 0.5× 441 1.2× 978 2.8× 82 4.8k
Barak A. Pearlmutter United States 28 1.2k 1.0× 2.1k 2.6× 325 0.7× 164 0.5× 574 1.6× 102 5.7k

Countries citing papers authored by Masa-aki Sato

Since Specialization
Citations

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

Fields of papers citing papers by Masa-aki Sato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Masa-aki Sato. 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 Masa-aki Sato. The network helps show where Masa-aki Sato may publish in the future.

Co-authorship network of co-authors of Masa-aki Sato

This figure shows the co-authorship network connecting the top 25 collaborators of Masa-aki Sato. A scholar is included among the top collaborators of Masa-aki Sato 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 Masa-aki Sato. Masa-aki Sato 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.
Murao, Osamu, et al.. (2025). Tsunami evacuation risk change associated with urban recovery in Banda Aceh after 2004 Aceh tsunami. International Journal of Disaster Risk Reduction. 121. 105400–105400. 1 indexed citations
2.
Sato, Masashi, Okito Yamashita, Masa-aki Sato, & Yoichi Miyawaki. (2018). Information spreading by a combination of MEG source estimation and multivariate pattern classification. PLoS ONE. 13(6). e0198806–e0198806. 14 indexed citations
3.
Takeda, Yusuke, Kentaro Yamanaka, Noriko Yamagishi, & Masa-aki Sato. (2014). Revealing Time-Unlocked Brain Activity from MEG Measurements by Common Waveform Estimation. PLoS ONE. 9(5). e98014–e98014. 3 indexed citations
4.
GOTO, Masao, et al.. (2012). Development of General Correlation for Heat Transfer in Single-Phase Turbulent Flow Inside Internally Helical-Grooved Tubes. Revista Trace. 22(4). 437–447. 2 indexed citations
5.
Umeda, Tatsuya, Kazuhiko Seki, Masa-aki Sato, et al.. (2012). Population Coding of Forelimb Joint Kinematics by Peripheral Afferents in Monkeys. PLoS ONE. 7(10). e47749–e47749. 10 indexed citations
6.
Sato, Masa-aki, et al.. (2008). Generation Method of AC Ampere Force Using Eddy Current inside a Non-Magnetic Thin Plate and it's Effect on Electro Dynamic Suspension. IEEJ Transactions on Industry Applications. 128(3). 236–243. 1 indexed citations
7.
Matsubara, Takamitsu, Jun Morimoto, Jun Nakanishi, Masa-aki Sato, & Kenji Doya. (2006). Learning CPG-based biped locomotion with a policy gradient method. 208–213. 4 indexed citations
8.
Fujiwara, Yusuke, Masa-aki Sato, Okito Yamashita, et al.. (2005). A method for removal of eye movement artifacts from MEG -- Simultaneous current source estimation of eyes and cortical activities from MEG and EOG data. 105(342). 43–48.
9.
Nakamura, Yutaka, et al.. (2004). Reinforcement learning for a CPG-driven biped robot. National Conference on Artificial Intelligence. 623–630. 47 indexed citations
10.
Nakamura, Yutaka, Masa-aki Sato, & Shin Ishii. (2004). Reinforcement Learning for Rhythmic Movements Using a Neural Oscillator Network. Transactions of the Institute of Electronics, Information and Communication Engineers. 87(3). 893–902. 2 indexed citations
11.
Yoshimoto, Junichiro, Shin Ishii, & Masa-aki Sato. (2003). On-Line EM Reinforcement Learning for Automatic Control of Continuous Dynamical Systems. Transactions of the Institute of Systems Control and Information Engineers. 16(5). 209–217. 2 indexed citations
12.
Sunahara, Hideki, et al.. (2002). IPCar : Building the Probe Car System with the Internet. IEICE Transactions on Communications. 85(4). 854. 5 indexed citations
13.
Sato, Masa-aki, et al.. (2000). DSRC STANDARDS AND ETC SYSTEMS DEVELOPMENT IN JAPAN. 3 indexed citations
14.
Dalal, Edul N., et al.. (1999). Effect of Selected Image Quality Attributes on Overall Preference.. PICS. 266–269. 2 indexed citations
15.
Sato, Masa-aki & Shin Ishii. (1998). On-Line EM Algorithm for Mixture of Local Experts. International Conference on Neural Information Processing. 1397–1401. 4 indexed citations
16.
Sato, Masa-aki & Shin Ishii. (1998). Reinforcement Learning Based on On-Line EM Algorithm. Neural Information Processing Systems. 11. 1052–1058. 19 indexed citations
17.
Rasmussen, Daniel, et al.. (1998). Image Quality Metrics: Applications and Requirements.. PICS. 174–178. 5 indexed citations
18.
Chikayama, Takashi, et al.. (1992). The Design of the PIMOS File System.. Future Generation Computer Systems. 278–285. 2 indexed citations
19.
Sato, Masa-aki, et al.. (1991). Learning Nonlinear Dynamics by Recurrent Neural Networks(Some Problems on the Theory of Dynamical Systems in Applied Sciences). Kyoto University Research Information Repository (Kyoto University). 760. 71–87. 1 indexed citations
20.
Sato, Masa-aki, et al.. (1974). COMTRAC FOR TOKYO-HAKATA SHINKANSEN. 15(1). 1 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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