Daniel G. Wakeman

778 citations
10 papers · 505 · h-index 7

Impact in

    • Functional Brain Connectivity Studies
    • Neural dynamics and brain function
    • EEG and Brain-Computer Interfaces
    • Neural and Behavioral Psychology Studies
    • Face Recognition and Perception
    • Advanced MRI Techniques and Applications
    • Advanced Neuroimaging Techniques and Applications

Papers in

Daniel G. Wakeman

10 papers receiving 500 citations

Peers

Daniel G. Wakeman
Comparison fields: 5 of 61
  • Cognitive Neuroscience 392
  • Radiology, Nuclear Medicine and Imaging 130
  • Signal Processing 60
  • Computational Mathematics 2
  • Experimental and Cognitive Psychology 38
Replace Eduardo Aubert-Vázquez with:
Eduardo Aubert-Vázquez Cuba
E. Best United States
Luís Freire Portugal
Arent de Jongh Netherlands
Hsiao‐Lan Sharon Wang Taiwan
A. Basilisco Italy
Ahmed Abou Elseoud Finland
Eduardo Castro United States
Federico Chella Italy
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Citations per field
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Citations per year

Countries citing papers authored by Daniel G. Wakeman

Since Specialization
Citations

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

Fields of papers citing papers by Daniel G. Wakeman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel G. Wakeman. 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 Daniel G. Wakeman. The network helps show where Daniel G. Wakeman may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel G. Wakeman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel G. Wakeman Line = papers co-authored together Daniel G. Wakeman links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 2010145
2 2016113
3 2015112
4 201181
5 200929
6 201614
7 20218
8 20241
9 20231
10 20201

About Daniel G. Wakeman

Daniel G. Wakeman is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Psychiatry and Mental health, Neurology and Infectious Diseases, having authored 10 papers that have together received 505 indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (5 papers), Neural dynamics and brain function (3 papers), Face Recognition and Perception (2 papers), Advanced Neuroimaging Techniques and Applications (2 papers), Advanced MRI Techniques and Applications (2 papers), Epilepsy research and treatment (1 paper), EEG and Brain-Computer Interfaces (1 paper) and Parkinson's Disease Mechanisms and Treatments (1 paper). The work is most often cited by research in Cognitive Neuroscience (392 citations), Radiology, Nuclear Medicine and Imaging (130 citations), Signal Processing (60 citations), Computational Mathematics (2 citations) and Experimental and Cognitive Psychology (38 citations). Daniel G. Wakeman has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Richard N. Henson, Olaf Hauk, Steven M. Stufflebeam, Hesheng Liu, Naoaki Tanaka, Linda Douw, Karl Friston, Vladimir Litvak, Joseph R. Madsen and Blaise F. D. Bourgeois. Their work appears in journals such as Magnetic Resonance in Medicine, Neuroscience, Neurology, NeuroImage Clinical and Neurourology and Urodynamics.

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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