Jun-ichiro Hirayama

586 total citations
24 papers, 318 citations indexed

About

Jun-ichiro Hirayama is a scholar working on Cognitive Neuroscience, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Jun-ichiro Hirayama has authored 24 papers receiving a total of 318 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Cognitive Neuroscience, 9 papers in Signal Processing and 7 papers in Artificial Intelligence. Recurrent topics in Jun-ichiro Hirayama's work include Neural dynamics and brain function (12 papers), Functional Brain Connectivity Studies (10 papers) and Blind Source Separation Techniques (8 papers). Jun-ichiro Hirayama is often cited by papers focused on Neural dynamics and brain function (12 papers), Functional Brain Connectivity Studies (10 papers) and Blind Source Separation Techniques (8 papers). Jun-ichiro Hirayama collaborates with scholars based in Japan, Finland and United Kingdom. Jun-ichiro Hirayama's co-authors include Michael U. Gutmann, Shin Ishii, Motoaki Kawanabe, Takeshi OGAWA, Atsunori Kanemura, Aapo Hyvärinen, Shigeyuki Ikeda, Hiroshi Morioka, Junichiro Yoshimoto and Shin‐ichi Maeda and has published in prestigious journals such as PLoS ONE, NeuroImage and Neural Computation.

In The Last Decade

Jun-ichiro Hirayama

24 papers receiving 309 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Jun-ichiro Hirayama 137 105 48 47 34 24 318
Pinki Kumari 96 0.7× 135 1.3× 36 0.8× 53 1.1× 85 2.5× 13 312
Anmol Gupta 103 0.8× 101 1.0× 36 0.8× 97 2.1× 19 0.6× 22 289
Sumit Soman 120 0.9× 108 1.0× 48 1.0× 40 0.9× 21 0.6× 38 349
Kyriakos Sgarbas 136 1.0× 171 1.6× 77 1.6× 25 0.5× 27 0.8× 55 529
Tsung-Yu Hsieh 122 0.9× 174 1.7× 31 0.6× 44 0.9× 14 0.4× 25 400
Monalisa Sarma 94 0.7× 115 1.1× 74 1.5× 64 1.4× 177 5.2× 69 503
Bernhard Schoelkopf 147 1.1× 237 2.3× 90 1.9× 35 0.7× 21 0.6× 19 483
Latika Singh 132 1.0× 84 0.8× 87 1.8× 62 1.3× 40 1.2× 47 418
Guanghui Yan 254 1.9× 66 0.6× 32 0.7× 37 0.8× 31 0.9× 73 521
Yuanyuan Shen 212 1.5× 114 1.1× 87 1.8× 41 0.9× 62 1.8× 10 432

Countries citing papers authored by Jun-ichiro Hirayama

Since Specialization
Citations

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

Fields of papers citing papers by Jun-ichiro Hirayama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun-ichiro Hirayama. 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 Jun-ichiro Hirayama. The network helps show where Jun-ichiro Hirayama may publish in the future.

Co-authorship network of co-authors of Jun-ichiro Hirayama

This figure shows the co-authorship network connecting the top 25 collaborators of Jun-ichiro Hirayama. A scholar is included among the top collaborators of Jun-ichiro Hirayama 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 Jun-ichiro Hirayama. Jun-ichiro Hirayama 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.
Hirayama, Jun-ichiro, et al.. (2023). Kinematic Motor Synergy Analysis to Understand Lock Dance Choreographies. 1–6. 1 indexed citations
2.
Kobler, Reinmar J., Jun-ichiro Hirayama, & Motoaki Kawanabe. (2022). Controlling The Fréchet Variance Improves Batch Normalization on the Symmetric Positive Definite Manifold. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 3863–3867. 2 indexed citations
3.
OGAWA, Takeshi, et al.. (2021). Asymmetric directed functional connectivity within the frontoparietal motor network during motor imagery and execution. NeuroImage. 247. 118794–118794. 19 indexed citations
4.
Kobler, Reinmar J., et al.. (2021). On the interpretation of linear Riemannian tangent space model parameters in M/EEG. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2021. 5909–5913. 7 indexed citations
5.
Takagi, Yu, Jun-ichiro Hirayama, & Saori Tanaka. (2019). State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits. NeuroImage. 201. 116036–116036. 8 indexed citations
6.
Hirayama, Jun-ichiro, et al.. (2018). Answering Mixed Type Questions about Daily Living Episodes. 4265–4271. 3 indexed citations
7.
Hirayama, Jun-ichiro, et al.. (2018). Generating an Event Timeline About Daily Activities From a Semantic Concept Stream. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 2 indexed citations
8.
Hirayama, Jun-ichiro, Aapo Hyvärinen, & Motoaki Kawanabe. (2017). SPLICE: Fully tractable hierarchical extension of ICA with pooling. Työväentutkimus Vuosikirja. 1491–1500. 4 indexed citations
9.
Hirayama, Jun-ichiro, Aapo Hyvärinen, Vesa Kiviniemi, Motoaki Kawanabe, & Okito Yamashita. (2016). Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis. PLoS ONE. 11(12). e0168180–e0168180. 4 indexed citations
10.
Hyvärinen, Aapo, Jun-ichiro Hirayama, Vesa Kiviniemi, & Motoaki Kawanabe. (2016). Orthogonal Connectivity Factorization: Interpretable Decomposition of Variability in Correlation Matrices. Neural Computation. 28(3). 445–484. 5 indexed citations
11.
Morioka, Hiroshi, Atsunori Kanemura, Jun-ichiro Hirayama, et al.. (2015). Learning a common dictionary for subject-transfer decoding with resting calibration. NeuroImage. 111. 167–178. 78 indexed citations
12.
OGAWA, Takeshi, Jun-ichiro Hirayama, Pankaj Gupta, et al.. (2015). Brain-machine interfaces for assistive smart homes: A feasibility study with wearable near-infrared spectroscopy. PubMed. 2015. 1107–10. 10 indexed citations
13.
Hirayama, Jun-ichiro, Takeshi OGAWA, & Aapo Hyvärinen. (2014). Simultaneous blind separation and clustering of coactivated EEG/MEG sources for analyzing spontaneous brain activity. PubMed. 100. 4932–4935. 3 indexed citations
14.
Hyvärinen, Aapo, Jun-ichiro Hirayama, & Motoaki Kawanabe. (2014). Dynamic connectivity factorization: Interpretable decompositions of non-stationarity. 1–4. 1 indexed citations
15.
Hirayama, Jun-ichiro & Aapo Hyvärinen. (2011). Structural equations and divisive normalization for energy-dependent component analysis. Neural Information Processing Systems. 24. 1872–1880. 4 indexed citations
16.
Gutmann, Michael U. & Jun-ichiro Hirayama. (2011). Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI 2011). 103 indexed citations
17.
Hirayama, Jun-ichiro, Shin‐ichi Maeda, & Shin Ishii. (2007). Markov and Semi-Markov Switching of Source Appearances for Nonstationary Independent Component Analysis. IEEE Transactions on Neural Networks. 18(5). 1326–1342. 14 indexed citations
18.
Hirayama, Jun-ichiro, Junichiro Yoshimoto, & Shin Ishii. (2006). Balancing plasticity and stability of on-line learning based on hierarchical Bayesian adaptation of forgetting factors. Neurocomputing. 69(16-18). 1954–1961. 4 indexed citations
19.
Hirayama, Jun-ichiro, Junichiro Yoshimoto, & Shin Ishii. (2004). Bayesian representation learning in the cortex regulated by acetylcholine. Neural Networks. 17(10). 1391–1400. 13 indexed citations
20.
Thawonmas, Ruck, Jun-ichiro Hirayama, & Fumiaki Takeda. (2002). RoboCup Agent Learning from Observations with Hierarchical Multiple Decision Trees. 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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