Miguel P. Eckstein

7.8k total citations
231 papers, 5.6k citations indexed

About

Miguel P. Eckstein is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Miguel P. Eckstein has authored 231 papers receiving a total of 5.6k indexed citations (citations by other indexed papers that have themselves been cited), including 126 papers in Cognitive Neuroscience, 87 papers in Computer Vision and Pattern Recognition and 41 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Miguel P. Eckstein's work include Visual perception and processing mechanisms (88 papers), Visual Attention and Saliency Detection (47 papers) and Neural dynamics and brain function (39 papers). Miguel P. Eckstein is often cited by papers focused on Visual perception and processing mechanisms (88 papers), Visual Attention and Saliency Detection (47 papers) and Neural dynamics and brain function (39 papers). Miguel P. Eckstein collaborates with scholars based in United States, Switzerland and United Kingdom. Miguel P. Eckstein's co-authors include Craig K. Abbey, Steven S. Shimozaki, Matthew Peterson, François Bochud, Binh T. Pham, M. Carrasco, James S. Whiting, Barry Giesbrecht, Albert J. Ahumada and Brent R. Beutter and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and NeuroImage.

In The Last Decade

Miguel P. Eckstein

210 papers receiving 5.5k citations

Peers

Miguel P. Eckstein
Comparison fields: 5 of 137
  • Cognitive Neuroscience 3.7k
  • Computer Vision and Pattern Recognition 1.7k
  • Radiology, Nuclear Medicine and Imaging 988
  • Experimental and Cognitive Psychology 619
  • Pulmonary and Respiratory Medicine 574
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Citations per field, relative to Miguel P. Eckstein
Miguel P. Eckstein · 1×
Citations per year, relative to Miguel P. Eckstein
Miguel P. Eckstein · 1×

Countries citing papers authored by Miguel P. Eckstein

Since Specialization
Citations

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

Fields of papers citing papers by Miguel P. Eckstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Miguel P. Eckstein. 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 Miguel P. Eckstein. The network helps show where Miguel P. Eckstein may publish in the future.

Co-authorship network of co-authors of Miguel P. Eckstein

This figure shows the co-authorship network connecting the top 25 collaborators of Miguel P. Eckstein. A scholar is included among the top collaborators of Miguel P. Eckstein 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 Miguel P. Eckstein. Miguel P. Eckstein 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
# Work Indexed citations
1 0
2 2
3 2
4 27
5 4
6 7
7 15
8 22
9
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes
4
10
Scene Inversion Slows the Rejection of False Positives through Saccade Exploration During Search.
3
11 0
12 17
13 34
14 1
15 4
16 6
17 1
18 13
19 378
20 0

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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