Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
200417.5k citationsArnaud Delorme, Scott MakeigJournal of Neuroscience Methodsprofile →
Removing electroencephalographic artifacts by blind source separation
20002.5k citationsTzyy‐Ping Jung, Scott Makeig et al.profile →
Analysis of fMRI data by blind separation into independent spatial components
19981.5k citationsMartin J. McKeown, Scott Makeig et al.Human Brain Mappingprofile →
This map shows the geographic impact of Scott Makeig'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 Scott Makeig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Makeig more than expected).
This network shows the impact of papers produced by Scott Makeig. 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 Scott Makeig. The network helps show where Scott Makeig may publish in the future.
Co-authorship network of co-authors of Scott Makeig
This figure shows the co-authorship network connecting the top 25 collaborators of Scott Makeig.
A scholar is included among the top collaborators of Scott Makeig 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 Scott Makeig. Scott Makeig is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Zhilin, Tzyy‐Ping Jung, Scott Makeig, & Bhaskar D. Rao. (2012). Low Energy Wireless Body-Area Networks for Fetal ECG Telemonitoring via the Framework of Block Sparse Bayesian Learning. arXiv (Cornell University).6 indexed citations
Delorme, Arnaud & Scott Makeig. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods. 134(1). 9–21.17538 indexed citations breakdown →
15.
Makeig, Scott, Tzyy‐Ping Jung, & Terrence J. Sejnowski. (2003). Having your voxels and timing them too. MIT Press eBooks. 195–207.5 indexed citations
16.
Jung, Tzyy‐Ping, Scott Makeig, Marissa Westerfield, et al.. (2001). Analysis and visualization of single‐trial event‐related potentials. Human Brain Mapping. 14(3). 166–185.541 indexed citations breakdown →
17.
Jung, Tzyy‐Ping, Scott Makeig, Marissa Westerfield, et al.. (1998). Analyzing and Visualizing Single-Trial Event-Related Potentials. Neural Information Processing Systems. 11. 118–124.42 indexed citations
18.
Jung, Tzyy‐Ping, Colin Humphries, Te-Won Lee, et al.. (1997). Extended ICA Removes Artifacts from Electroencephalographic Recordings. Neural Information Processing Systems. 10. 894–900.330 indexed citations
19.
Makeig, Scott, Tzyy‐Ping Jung, & Terrence J. Sejnowski. (1995). Using Feedforward Neural Networks to Monitor Alertness from Changes in EEG Correlation and Coherence. Neural Information Processing Systems. 8. 931–937.28 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.