Felix Bießmann
- Cognitive Neuroscience top 1%
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Cellular and Molecular Neuroscience top 10%
- Signal Processing top 5%
- Co-authors
- Frank C. MeineckeSven DähneJohn–Dylan HaynesKai GörgenBenjamin BlankertzStefan HaufePhilipp SchmidtSebastian Schelter
- Topics
- Neural dynamics and brain function (10 papers)Machine Learning and Data Classification (7 papers)EEG and Brain-Computer Interfaces (7 papers)
- Journals
- SHILAP Revista de lepidopterologíaNeuroImageRadiology
- Partner nations
- GermanyUnited StatesSouth Korea
In The Last Decade
Felix Bießmann
45 papers receiving 2.4k citations
Hit Papers
Peers
Comparison fields: 5 of 162
- Cognitive Neuroscience 1.3k
- Artificial Intelligence 590
- Radiology, Nuclear Medicine and Imaging 263
- Cellular and Molecular Neuroscience 233
- Signal Processing 202
Countries citing papers authored by Felix Bießmann
This map shows the geographic impact of Felix Bießmann'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 Felix Bießmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felix Bießmann more than expected).
Fields of papers citing papers by Felix Bießmann
This network shows the impact of papers produced by Felix Bießmann. 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 Felix Bießmann. The network helps show where Felix Bießmann may publish in the future.
Co-authorship network of co-authors of Felix Bießmann
This figure shows the co-authorship network connecting the top 25 collaborators of Felix Bießmann. A scholar is included among the top collaborators of Felix Bießmann 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 Felix Bießmann. Felix Bießmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 7 | |
| 6 | 10 | |
| 7 | 18 | |
| 8 | 61 | |
| 9 | 9 | |
| 10 | 3 | |
| 11 | 149 | |
| 12 | 117 | |
| 13 | DataWig: Missing Value Imputation for Tables | 67 |
| 14 | On Challenges in Machine Learning Model Management | 85 |
| 15 | On the interpretation of weight vectors of linear models in multivariate neuroimagingbreakdown → | 850 |
| 16 | 19 | |
| 17 | 35 | |
| 18 | 114 | |
| 19 | 57 | |
| 20 | Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance | 40 |
About Felix Bießmann
Felix Bießmann is a scholar working on Health Informatics, Cognitive Neuroscience and Signal Processing, having authored 50 papers that have together received 2.5k indexed citations. Recurring topics across this work include Neural dynamics and brain function (10 papers), Machine Learning and Data Classification (7 papers) and EEG and Brain-Computer Interfaces (7 papers). The work is most often cited by research in Health Informatics (169 citations), Cognitive Neuroscience (1.3k citations) and Signal Processing (202 citations). Felix Bießmann has collaborated with scholars based in Germany, United States and South Korea. Frequent co-authors include Frank C. Meinecke, Sven Dähne, John–Dylan Haynes, Kai Görgen, Benjamin Blankertz, Stefan Haufe, Philipp Schmidt, Sebastian Schelter, Timm Teubner and Klaus‐Robert Müller. Their work appears in journals such as SHILAP Revista de lepidopterología, NeuroImage and Radiology.
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.