Journal of Neural Engineering

3.5k papers and 104.0k indexed citations i.

About

The 3.5k papers published in Journal of Neural Engineering in the last decades have received a total of 104.0k indexed citations. Papers published in Journal of Neural Engineering usually cover Cognitive Neuroscience (2.6k papers), Cellular and Molecular Neuroscience (1.9k papers) and Biomedical Engineering (851 papers) specifically the topics of EEG and Brain-Computer Interfaces (2.0k papers), Neuroscience and Neural Engineering (1.8k papers) and Neural dynamics and brain function (1.0k papers). The most active scholars publishing in Journal of Neural Engineering are Jonathan R. Wolpaw, Warren M. Grill, Fabien Lotte, Xiaorong Gao, Marco Congedo, Dario Farina, Shangkai Gao, Gerwin Schalk, José L. Contreras-Vidal and Daryl R. Kipke.

In The Last Decade

Fields of papers published in Journal of Neural Engineering

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Journal of Neural Engineering. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Journal of Neural Engineering.

Countries where authors publish in Journal of Neural Engineering

Since Specialization
Citations

This map shows the geographic impact of research published in Journal of Neural Engineering. 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 papers published in Journal of Neural Engineering with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Journal of Neural Engineering more than expected).

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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