Sen Song
- Cognitive Neuroscience top 0.5%
- Neural dynamics and brain function 24
- EEG and Brain-Computer Interfaces 6
- Cellular and Molecular Neuroscience top 0.2%
- Neuroscience and Neuropharmacology Research 13
- Neuroscience and Neural Engineering 7
- Photoreceptor and optogenetics research 6
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- Advanced Memory and Neural Computing 15
- Artificial Intelligence top 1%
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- Genetic diversity and population structure 9
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- Identification and Quantification in Food 6
Sen Song
86 papers receiving 8.2k citations
Hit Papers
Peers
Comparison fields: 5 of 170
- Cognitive Neuroscience 3.9k
- Cellular and Molecular Neuroscience 3.4k
- Electrical and Electronic Engineering 3.8k
- Computer Vision and Pattern Recognition 846
- Artificial Intelligence 1.2k
Countries citing papers authored by Sen Song
This map shows the geographic impact of Sen Song'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 Sen Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sen Song more than expected).
Fields of papers citing papers by Sen Song
This network shows the impact of papers produced by Sen Song. 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 Sen Song. The network helps show where Sen Song may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Sen Song, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 10 | |
| 5 | 2024 | 31 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 2 | |
| 8 | Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognitionbreakdown → | 2022 | 165 |
| 9 | 2022 | 7 | |
| 10 | 2022 | 18 | |
| 11 | 2021 | 81 | |
| 12 | 2020 | 106 | |
| 13 | 2020 | 209 | |
| 14 | 2018 | 52 | |
| 15 | 2014 | 118 | |
| 16 | 2013 | 98 | |
| 17 | 2013 | 20 | |
| 18 | 2007 | 42 | |
| 19 | 2000 | 6 | |
| 20 | Multiple agents from the bottom up: the interaction lab's robot competition effort | 1997 | 1 |
About Sen Song
Sen Song is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Biophysics, having authored 87 papers that have together received 8.4k indexed citations. Recurring topics across this work include Neural dynamics and brain function (24 papers), Advanced Memory and Neural Computing (15 papers), Neuroscience and Neuropharmacology Research (13 papers), Genetic diversity and population structure (9 papers), Neuroscience and Neural Engineering (7 papers), Photoreceptor and optogenetics research (6 papers), EEG and Brain-Computer Interfaces (6 papers) and Identification and Quantification in Food (6 papers). The work is most often cited by research in Cognitive Neuroscience (3.9k citations), Cellular and Molecular Neuroscience (3.4k citations) and Electrical and Electronic Engineering (3.8k citations). Sen Song has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include L. F. Abbott, Kenneth D. Miller, Sacha B. Nelson, P. Jesper Sjöström, Dmitri B. Chklovskii, L. F. Abbott, Xinke Shen, Liang Liu, Scott V. Edwards and Shaoyuan Wu. Their work appears in journals such as Nature Communications, Journal of Neuroscience, Neuron, Briefings in Bioinformatics and Neurocomputing.
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.