Sebastian Stober

1.3k citations
60 papers · 680 indexed · h-index 12
Topics
Music and Audio Processing (20 papers)EEG and Brain-Computer Interfaces (11 papers)Video Analysis and Summarization (9 papers)

In The Last Decade

Sebastian Stober

57 papers receiving 653 citations

Peers

Sebastian Stober
Comparison fields: 5 of 94
  • Cognitive Neuroscience 349
  • Computer Vision and Pattern Recognition 163
  • Signal Processing 157
  • Artificial Intelligence 95
  • Radiology, Nuclear Medicine and Imaging 71
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Citations per field
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Citations per year

Countries citing papers authored by Sebastian Stober

Since Specialization
Citations

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

Fields of papers citing papers by Sebastian Stober

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sebastian Stober. 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 Sebastian Stober. The network helps show where Sebastian Stober may publish in the future.

Co-authorship network of co-authors of Sebastian Stober

This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Stober. A scholar is included among the top collaborators of Sebastian Stober 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 Sebastian Stober. Sebastian Stober 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
#WorkIndexed citations
1 0
2 3
3 6
4 3
5 2
6 28
7 5
8 14
9 8
10 95
11 64
12 58
13 7
14
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings
46
15 11
16 1
17 4
18
Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
1
19
SUPPORTING FOLK-SONG RESEARCH BY AUTOMATIC METRIC LEARNING AND RANKING
8
20
User-Adaptive Music Information Retrieval.
2

About Sebastian Stober

Sebastian Stober is a scholar working on Signal Processing, Health Informatics and Computer Vision and Pattern Recognition, having authored 60 papers that have together received 680 indexed citations. Recurring topics across this work include Music and Audio Processing (20 papers), EEG and Brain-Computer Interfaces (11 papers) and Video Analysis and Summarization (9 papers). The work is most often cited by research in Cognitive Neuroscience (349 citations), Health Informatics (21 citations) and Signal Processing (157 citations). Sebastian Stober has collaborated with scholars based in Germany, United States and Spain. Frequent co-authors include Andreas Nürnberger, Amirali Vahid, Christian Beste, Jessica A. Grahn, Marc Dewey, Federico Biavati, Daniel J. Cameron, Florian Michallek, Veit Roessner and Annet Bluschke. Their work appears in journals such as Scientific Reports, Frontiers in Psychology and Journal of Medical Internet Research.

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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